The data for the fingerling weight, harvest weight and the stock numbers were processed to remove outliers.
############################################################
# Clean data by removal of outliers using percentiles
############################################################
pLower = 0.005
pUpper = 0.995
RemovalProbs = c(pLower, pUpper)
if (RemoveOutliers){
if (RemoveOutliersFingerlings){
#Strip out the lower 2.5% and upper 2.5% of the fingerling and harvest weights
SurveyData$Tilapia.Fingerlings.Weight..g.[SurveyData$Tilapia.Fingerlings.Weight..g. == 0] <- NA
QQ_Tila_Finger <- quantile(SurveyData$Tilapia.Fingerlings.Weight..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Tilapia.Fingerlings.Weight..g. <= QQ_Tila_Finger[1] | SurveyData$Tilapia.Fingerlings.Weight..g. >= QQ_Tila_Finger[2], na.rm = T)," outliers from tilapia fingerling weights"))
SurveyData$Tilapia.Fingerlings.Weight..g.[SurveyData$Tilapia.Fingerlings.Weight..g. <= QQ_Tila_Finger[1]] <- NA
SurveyData$Tilapia.Fingerlings.Weight..g.[SurveyData$Tilapia.Fingerlings.Weight..g. >= QQ_Tila_Finger[2]] <- NA
SurveyData$Pangasius.Fingerlings.Weight..g.[SurveyData$Pangasius.Fingerlings.Weight..g. == 0] <- NA
QQ_Pang_Finger <- quantile(SurveyData$Pangasius.Fingerlings.Weight..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Pangasius.Fingerlings.Weight..g. <= QQ_Pang_Finger[1] | SurveyData$Pangasius.Fingerlings.Weight..g. >= QQ_Pang_Finger[2], na.rm = T)," outliers from pangasius fingerling weights"))
SurveyData$Pangasius.Fingerlings.Weight..g.[SurveyData$Pangasius.Fingerlings.Weight..g. <= QQ_Pang_Finger[1]] <- NA
SurveyData$Pangasius.Fingerlings.Weight..g.[SurveyData$Pangasius.Fingerlings.Weight..g. >= QQ_Pang_Finger[2]] <- NA
SurveyData$Carp.Fingerlings.Weight..g.[SurveyData$Carp.Fingerlings.Weight..g. == 0] <- NA
QQ_Carp_Finger <- quantile(SurveyData$Carp.Fingerlings.Weight..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Carp.Fingerlings.Weight..g. <= QQ_Carp_Finger[1] | SurveyData$Carp.Fingerlings.Weight..g. >= QQ_Carp_Finger[2], na.rm = T)," outliers from carp fingerling weights"))
SurveyData$Carp.Fingerlings.Weight..g.[SurveyData$Carp.Fingerlings.Weight..g. <= QQ_Carp_Finger[1]] <- NA
SurveyData$Carp.Fingerlings.Weight..g.[SurveyData$Carp.Fingerlings.Weight..g. >= QQ_Carp_Finger[2]] <- NA
}
if (RemoveOutliersHarvest){
#Strip out the lower 2.5% and upper 2.5% of the harvest weights
SurveyData$Tilapia.Weight.at.Harvest..g.[SurveyData$Tilapia.Weight.at.Harvest..g. == 0] <- NA
QQ_Tila_Harvest <- quantile(SurveyData$Tilapia.Weight.at.Harvest..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Tilapia.Weight.at.Harvest..g. <= QQ_Tila_Harvest[1] | SurveyData$Tilapia.Weight.at.Harvest..g. >= QQ_Tila_Harvest[2], na.rm = T)," outliers from tilapia harvest weights"))
SurveyData$Tilapia.Weight.at.Harvest..g.[SurveyData$Tilapia.Weight.at.Harvest..g. <= QQ_Tila_Harvest[1]] <- NA
SurveyData$Tilapia.Weight.at.Harvest..g.[SurveyData$Tilapia.Weight.at.Harvest..g. >= QQ_Tila_Harvest[2]] <- NA
SurveyData$Pangasius.Weight.at.Harvest..g.[SurveyData$Pangasius.Weight.at.Harvest..g. == 0] <- NA
QQ_Pang_Harvest <- quantile(SurveyData$Pangasius.Weight.at.Harvest..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Pangasius.Weight.at.Harvest..g. <= QQ_Pang_Harvest[1] | SurveyData$Pangasius.Weight.at.Harvest..g. >= QQ_Pang_Harvest[2], na.rm = T)," outliers from pangasius harvest weights"))
SurveyData$Pangasius.Weight.at.Harvest..g.[SurveyData$Pangasius.Weight.at.Harvest..g. <= QQ_Pang_Harvest[1]] <- NA
SurveyData$Pangasius.Weight.at.Harvest..g.[SurveyData$Pangasius.Weight.at.Harvest..g. >= QQ_Pang_Harvest[2]] <- NA
SurveyData$Carp.Weight.at.Harvest..g.[SurveyData$Carp.Weight.at.Harvest..g. == 0] <- NA
QQ_Carp_Harvest <- quantile(SurveyData$Carp.Weight.at.Harvest..g., probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Carp.Weight.at.Harvest..g. <= QQ_Carp_Harvest[1] | SurveyData$Carp.Weight.at.Harvest..g. >= QQ_Carp_Harvest[2], na.rm = T)," outliers from carp harvest weights"))
SurveyData$Carp.Weight.at.Harvest..g.[SurveyData$Carp.Weight.at.Harvest..g. <= QQ_Carp_Harvest[1]] <- NA
SurveyData$Carp.Weight.at.Harvest..g.[SurveyData$Carp.Weight.at.Harvest..g. >= QQ_Carp_Harvest[2]] <- NA
}
if (RemoveOutliersStock){
# Repeat outlier removal for number of stocked fish
SurveyData$Number.of.Tilapia.Stocked[SurveyData$Number.of.Tilapia.Stocked == 0] <- NA
QQ_Tila_Stock <- quantile(SurveyData$Number.of.Tilapia.Stocked, probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Number.of.Tilapia.Stocked <= QQ_Tila_Stock[1] | SurveyData$Number.of.Tilapia.Stocked >= QQ_Tila_Stock[2], na.rm = T)," outliers from tilapia stock numbers"))
Outliers <- which(SurveyData$Number.of.Tilapia.Stocked <= QQ_Tila_Stock[1] | SurveyData$Number.of.Tilapia.Stocked >= QQ_Tila_Stock[2])
SurveyData <- SurveyData[!Outliers, ]
SurveyData$Number.of.Pangasius.Stocked[SurveyData$Number.of.Pangasius.Stocked == 0] <- NA
QQ_Pang_Stock <- quantile(SurveyData$Number.of.Pangasius.Stocked, probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Number.of.Pangasius.Stocked <= QQ_Pang_Stock[1] | SurveyData$Number.of.Pangasius.Stocked >= QQ_Pang_Stock[2], na.rm = T)," outliers from pangasius stock numbers"))
Outliers <- which(SurveyData$Number.of.Pangasius.Stocked <= QQ_Pang_Stock[1] | SurveyData$Number.of.Pangasius.Stocked >= QQ_Pang_Stock[2])
SurveyData <- SurveyData[!Outliers, ]
SurveyData$Number.of.Carp.Fish.Stocked[SurveyData$Number.of.Carp.Fish.Stocked == 0] <- NA
QQ_Carp_Stock <- quantile(SurveyData$Number.of.Carp.Fish.Stocked, probs = RemovalProbs, na.rm = TRUE)
print(paste0("Removing ",
sum(SurveyData$Number.of.Carp.Fish.Stocked <= QQ_Carp_Stock[1] | SurveyData$Number.of.Carp.Fish.Stocked >= QQ_Carp_Stock[2], na.rm = T)," outliers from carp stock numbers"))
Outliers <- which(SurveyData$Number.of.Carp.Fish.Stocked <= QQ_Carp_Stock[1] | SurveyData$Number.of.Carp.Fish.Stocked >= QQ_Carp_Stock[2])
SurveyData <- SurveyData[!Outliers, ]
}
}## [1] "Removing 8 outliers from tilapia fingerling weights"
## [1] "Removing 2 outliers from pangasius fingerling weights"
## [1] "Removing 21 outliers from carp fingerling weights"
## [1] "Removing 2 outliers from tilapia harvest weights"
## [1] "Removing 2 outliers from pangasius harvest weights"
## [1] "Removing 4 outliers from carp harvest weights"
## [1] "Removing 2 outliers from tilapia stock numbers"
## [1] "Removing 2 outliers from pangasius stock numbers"
## [1] "Removing 4 outliers from carp stock numbers"# Calculate overall stock in ponds
SurveyData[, Overall_Stock := rowSums(.SD, na.rm = TRUE), .SDcols = c("Number.of.Pangasius.Stocked", "Number.of.Tilapia.Stocked", "Number.of.Carp.Fish.Stocked", "Number.of.Other.Fish.Stocked")]
SurveyData[, Overall_Density := (Overall_Stock/(Pond.surface.area * 40.46))]
SurveyData[, Tila_Density := (Number.of.Tilapia.Stocked/(Pond.surface.area * 40.46))]
SurveyData[, Pang_Density := (Number.of.Pangasius.Stocked/(Pond.surface.area * 40.46))]
SurveyData[, Carp_Density := (Number.of.Carp.Fish.Stocked/(Pond.surface.area * 40.46))]
SurveyData[, Othe_Density := (Number.of.Other.Fish.Stocked/(Pond.surface.area * 40.46))]
# The producitivty is based on the harvest weight - fingerling weight / fingerling weight * 100
prod_Tila <- ((SurveyData$Tilapia.Weight.at.Harvest..g. - SurveyData$Tilapia.Fingerlings.Weight..g.)/SurveyData$Tilapia.Fingerlings.Weight..g.) * 100
#prod_Tila[SurveyData$Tilapia.Fingerlings.Weight..g.< 1] <- NAThis markdown documents the data analysis for the Aquaculture International Paper using farmers questionnaire data to produce a model of the economics of finfish production in Mymensingh.
#########################################################
#
# Get the age of the ponds for those farms in Mymensingh
#
#########################################################
library(kableExtra)## Warning: package 'kableExtra' was built under R version 4.0.5# Get the age for all the farm ponds
Age_AllPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond
MeanAge_AllPonds <- mean(Age_AllPonds, na.rm = TRUE)
Percent_Under5_All <- sum(Age_AllPonds < 5)/length(Age_AllPonds) * 100
Percent_Under10_All <- sum(Age_AllPonds < 10)/length(Age_AllPonds) * 100
# Age of ponds in Mymensingh by species
Tila_Age_AllPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[!is.na(SurveyData$Number.of.Tilapia.Stocked)]
Pang_Age_AllPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[!is.na(SurveyData$Number.of.Pangasius.Stocked)]
Carp_Age_AllPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[!is.na(SurveyData$Number.of.Carp.Fish.Stocked)]
# Repeat but just for the Mymensingh district
Age_MymenPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[SurveyData$MymenFarmers == 1]
MeanAge_MymenPonds <- mean(Age_MymenPonds, na.rm = TRUE)
Percent_Under5_MymenPonds <- sum(Age_MymenPonds < 5)/length(Age_MymenPonds) * 100
Percent_Under10_MymenPonds <- sum(Age_MymenPonds < 10)/length(Age_MymenPonds) * 100
# Age of ponds in Mymensingh by species
Tila_Age_MymenPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Tilapia.Stocked)]
Pang_Age_MymenPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Pangasius.Stocked)]
Carp_Age_MymenPonds <- surveyYear - SurveyData$Year.Culture.started.in.Pond[SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Carp.Fish.Stocked)]
BasicStats <- data.frame(rbind(
c("Mean age of all ponds", mean(Age_AllPonds, na.rm = TRUE)),
c("Age of all ponds STD", sd(Age_AllPonds, na.rm = TRUE)),
c("Mean age of all tilapia ponds", mean(Tila_Age_AllPonds, na.rm = TRUE)),
c("Age of all tilapia ponds STD", sd(Tila_Age_AllPonds, na.rm = TRUE)),
c("Mean age of all pang ponds", mean(Pang_Age_AllPonds, na.rm = TRUE)),
c("Age of all pang ponds STD", sd(Pang_Age_AllPonds, na.rm = TRUE)),
c("Mean age of all carp ponds", mean(Carp_Age_AllPonds, na.rm = TRUE)),
c("Age of all carp ponds STD", sd(Carp_Age_AllPonds, na.rm = TRUE)),
c("Mean age of Mymensingh district ponds", mean(Age_MymenPonds, na.rm = TRUE)),
c("Age of Mymensingh district ponds STD", sd(Age_MymenPonds, na.rm = TRUE)),
c("Mean age of all tilapia Mymensingh ponds", mean(Tila_Age_MymenPonds, na.rm = TRUE)),
c("Age of all tilapia Mymensingh ponds STD", sd(Tila_Age_MymenPonds, na.rm = TRUE)),
c("Mean age of all pang Mymensingh ponds", mean(Pang_Age_MymenPonds, na.rm = TRUE)),
c("Age of all pang Mymensingh ponds STD", sd(Pang_Age_MymenPonds, na.rm = TRUE)),
c("Mean age of all carp Mymensingh ponds", mean(Carp_Age_MymenPonds, na.rm = TRUE)),
c("Age of all carp Mymensingh ponds STD", sd(Carp_Age_MymenPonds, na.rm = TRUE))))
colnames(BasicStats) <- c("Parameter", "Value")
BasicStatsTable <- kbl(BasicStats, caption = "Pond ages") %>%
kable_paper(full_width = TRUE)
#############################################################
#
# Fish species stocked
#############################################################
Tila_ponds_All <- sum(!is.na(SurveyData$Number.of.Tilapia.Stocked))
Carp_ponds_All <- sum(!is.na(SurveyData$Number.of.Carp.Fish.Stocked))
Pang_ponds_All <- sum(!is.na(SurveyData$Number.of.Pangasius.Stocked))
Othe_ponds_All <- sum(!is.na(SurveyData$Number.of.Other.Fish.Stocked))
Tila_ponds_Mymen <- sum(SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Tilapia.Stocked))
Carp_ponds_Mymen <- sum(SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Carp.Fish.Stocked))
Pang_ponds_Mymen <- sum(SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Pangasius.Stocked))
Othe_ponds_Mymen <- sum(SurveyData$MymenFarmers == 1 & !is.na(SurveyData$Number.of.Other.Fish.Stocked))
##############################################################
# Get stocking densities
##############################################################
StockDensity_DF <- data.frame(rbind(
c("Overall Mean Stocking Density per ha", SurveyData[, mean(Overall_Density, na.rm = TRUE)]),
c("Overall STD Stocking Density per ha", SurveyData[, sd(Overall_Density, na.rm = TRUE)]),
c("Overall Range Stocking Density per ha", paste(SurveyData[, min(Overall_Density, na.rm = TRUE)],":", SurveyData[, max(Overall_Density, na.rm = TRUE)])),
c("Tilapia Mean Stocking Density per ha", SurveyData[, mean(Tila_Density, na.rm = TRUE)]),
c("Tilapia STD Stocking Density per ha", SurveyData[, sd(Tila_Density, na.rm = TRUE)]),
c("Tilapia Range Stocking Density per ha", paste(SurveyData[, min(Tila_Density, na.rm = TRUE)],":", SurveyData[, max(Tila_Density, na.rm = TRUE)])),
c("Pangasius Mean Stocking Density per ha", SurveyData[, mean(Pang_Density, na.rm = TRUE)]),
c("Pangasius STD Stocking Density per ha", SurveyData[, sd(Pang_Density, na.rm = TRUE)]),
c("Pangasius Range Stocking Density per ha", paste(SurveyData[, min(Pang_Density, na.rm = TRUE)],":", SurveyData[, max(Pang_Density, na.rm = TRUE)])),
c("Carp Mean Stocking Density per ha", SurveyData[, mean(Carp_Density, na.rm = TRUE)]),
c("Carp STD Stocking Density per ha", SurveyData[, sd(Carp_Density, na.rm = TRUE)]),
c("Carp Range Stocking Density per ha", paste(SurveyData[, min(Carp_Density, na.rm = TRUE)],":", SurveyData[, max(Carp_Density, na.rm = TRUE)]))))
colnames(StockDensity_DF) <- c("Stock Parameter", "Value")
StockDensity_DF_Table <- kbl(StockDensity_DF, caption = "Stocking Densities") %>%
kable_paper(full_width = TRUE)
##############################################################
# Stratify the stocking densities according to culture method
##############################################################
StockDensityBySpecies_TPC <- data.frame(rbind(
c("TPC: Overall Mean Stocking Density", SurveyData[PolyCult == "TPC", mean(Overall_Density, na.rm = TRUE)]),
c("TPC: Overall STD Stocking Density", SurveyData[PolyCult == "TPC", sd(Overall_Density, na.rm = TRUE)]),
c("TPC: Overall Range Stocking Density", paste(SurveyData[PolyCult == "TPC", min(Overall_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(Overall_Density, na.rm = TRUE)])),
c("TPC: Tilapia Mean Stocking Density", SurveyData[PolyCult == "TPC", mean(Tila_Density, na.rm = TRUE)]),
c("TPC: Tilapia STD Stocking Density", SurveyData[PolyCult == "TPC", sd(Tila_Density, na.rm = TRUE)]),
c("TPC: Tilapia Range Stocking Density", paste(SurveyData[PolyCult == "TPC", min(Tila_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(Tila_Density, na.rm = TRUE)])),
c("TPC: Pangasius Mean Stocking Density", SurveyData[PolyCult == "TPC", mean(Pang_Density, na.rm = TRUE)]),
c("TPC: Pangasius STD Stocking Density", SurveyData[PolyCult == "TPC", sd(Pang_Density, na.rm = TRUE)]),
c("TPC: Pangasius Range Stocking Density", paste(SurveyData[PolyCult == "TPC", min(Pang_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(Pang_Density, na.rm = TRUE)])),
c("TPC: Carp Mean Stocking Density", SurveyData[PolyCult == "TPC", mean(Carp_Density, na.rm = TRUE)]),
c("TPC: Carp STD Stocking Density", SurveyData[PolyCult == "TPC", sd(Carp_Density, na.rm = TRUE)]),
c("TPC: Carp Range Stocking Density", paste(SurveyData[PolyCult == "TPC", min(Carp_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(Carp_Density, na.rm = TRUE)]))))
colnames(StockDensityBySpecies_TPC) <- c("Stock Parameter", "Value")
StockDensityBySpecies_TPC_Table <- kbl(StockDensityBySpecies_TPC, caption = "Stocking Densities for TPC Ponds") %>%
kable_paper(full_width = TRUE)
StockDensityBySpecies_TC <- data.frame(rbind(
c("TC: Overall Mean Stocking Density", SurveyData[PolyCult == "TC", mean(Overall_Density, na.rm = TRUE)]),
c("TC: Overall STD Stocking Density", SurveyData[PolyCult == "TC", sd(Overall_Density, na.rm = TRUE)]),
c("TC: Overall Range Stocking Density", paste(SurveyData[PolyCult == "TC", min(Overall_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(Overall_Density, na.rm = TRUE)])),
c("TC: Tilapia Mean Stocking Density", SurveyData[PolyCult == "TC", mean(Tila_Density, na.rm = TRUE)]),
c("TC: Tilapia STD Stocking Density", SurveyData[PolyCult == "TC", sd(Tila_Density, na.rm = TRUE)]),
c("TC: Tilapia Range Stocking Density", paste(SurveyData[PolyCult == "TC", min(Tila_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(Tila_Density, na.rm = TRUE)])),
c("TC: Pangasius Mean Stocking Density", SurveyData[PolyCult == "TC", mean(Pang_Density, na.rm = TRUE)]),
c("TC: Pangasius STD Stocking Density", SurveyData[PolyCult == "TC", sd(Pang_Density, na.rm = TRUE)]),
c("TC: Pangasius Range Stocking Density", paste(SurveyData[PolyCult == "TC", min(Pang_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(Pang_Density, na.rm = TRUE)])),
c("TC: Carp Mean Stocking Density", SurveyData[PolyCult == "TC", mean(Carp_Density, na.rm = TRUE)]),
c("TC: Carp STD Stocking Density", SurveyData[PolyCult == "TC", sd(Carp_Density, na.rm = TRUE)]),
c("TC: Carp Range Stocking Density", paste(SurveyData[PolyCult == "TC", min(Carp_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(Carp_Density, na.rm = TRUE)]))))## Warning in min(Pang_Density, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in max(Pang_Density, na.rm = TRUE): no non-missing arguments to max;
## returning -Infcolnames(StockDensityBySpecies_TC) <- c("Stock Parameter", "Value")
StockDensityBySpecies_TC_Table <- kbl(StockDensityBySpecies_TC, caption = "Stocking Densities for TC Ponds") %>%
kable_paper(full_width = TRUE)
StockDensityBySpecies_PC <- data.frame(rbind(
c("PC: Overall Mean Stocking Density", SurveyData[PolyCult == "PC", mean(Overall_Density, na.rm = TRUE)]),
c("PC: Overall STD Stocking Density", SurveyData[PolyCult == "PC", sd(Overall_Density, na.rm = TRUE)]),
c("PC: Overall Range Stocking Density", paste(SurveyData[PolyCult == "PC", min(Overall_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(Overall_Density, na.rm = TRUE)])),
c("PC: Tilapia Mean Stocking Density", SurveyData[PolyCult == "PC", mean(Tila_Density, na.rm = TRUE)]),
c("PC: Tilapia STD Stocking Density", SurveyData[PolyCult == "PC", sd(Tila_Density, na.rm = TRUE)]),
c("PC: Tilapia Range Stocking Density", paste(SurveyData[PolyCult == "PC", min(Tila_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(Tila_Density, na.rm = TRUE)])),
c("PC: Pangasius Mean Stocking Density", SurveyData[PolyCult == "PC", mean(Pang_Density, na.rm = TRUE)]),
c("PC: Pangasius STD Stocking Density", SurveyData[PolyCult == "PC", sd(Pang_Density, na.rm = TRUE)]),
c("PC: Pangasius Range Stocking Density", paste(SurveyData[PolyCult == "PC", min(Pang_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(Pang_Density, na.rm = TRUE)])),
c("PC: Carp Mean Stocking Density", SurveyData[PolyCult == "PC", mean(Carp_Density, na.rm = TRUE)]),
c("PC: Carp STD Stocking Density", SurveyData[PolyCult == "PC", sd(Carp_Density, na.rm = TRUE)]),
c("PC: Carp Range Stocking Density", paste(SurveyData[PolyCult == "PC", min(Carp_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(Carp_Density, na.rm = TRUE)]))))## Warning in min(Tila_Density, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in max(Tila_Density, na.rm = TRUE): no non-missing arguments to max;
## returning -Infcolnames(StockDensityBySpecies_PC) <- c("Stock Parameter", "Value")
StockDensityBySpecies_PC_Table <- kbl(StockDensityBySpecies_PC, caption = "Stocking Densities for PC Ponds") %>%
kable_paper(full_width = TRUE)
StockDensityBySpecies_TP <- data.frame(rbind(
c("TP: Overall Mean Stocking Density", SurveyData[PolyCult == "TP", mean(Overall_Density, na.rm = TRUE)]),
c("TP: Overall STD Stocking Density", SurveyData[PolyCult == "TP", sd(Overall_Density, na.rm = TRUE)]),
c("TP: Overall Range Stocking Density", paste(SurveyData[PolyCult == "TP", min(Overall_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(Overall_Density, na.rm = TRUE)])),
c("TP: Tilapia Mean Stocking Density", SurveyData[PolyCult == "TP", mean(Tila_Density, na.rm = TRUE)]),
c("TP: Tilapia STD Stocking Density", SurveyData[PolyCult == "TP", sd(Tila_Density, na.rm = TRUE)]),
c("TP: Tilapia Range Stocking Density", paste(SurveyData[PolyCult == "TP", min(Tila_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(Tila_Density, na.rm = TRUE)])),
c("TP: Pangasius Mean Stocking Density", SurveyData[PolyCult == "TP", mean(Pang_Density, na.rm = TRUE)]),
c("TP: Pangasius STD Stocking Density", SurveyData[PolyCult == "TP", sd(Pang_Density, na.rm = TRUE)]),
c("TP: Pangasius Range Stocking Density", paste(SurveyData[PolyCult == "TP", min(Pang_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(Pang_Density, na.rm = TRUE)])),
c("TP: Carp Mean Stocking Density", SurveyData[PolyCult == "TP", mean(Carp_Density, na.rm = TRUE)]),
c("TP: Carp STD Stocking Density", SurveyData[PolyCult == "TP", sd(Carp_Density, na.rm = TRUE)]),
c("TP: Carp Range Stocking Density", paste(SurveyData[PolyCult == "TP", min(Carp_Density, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(Carp_Density, na.rm = TRUE)]))))## Warning in min(Carp_Density, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in max(Carp_Density, na.rm = TRUE): no non-missing arguments to max;
## returning -Infcolnames(StockDensityBySpecies_TP) <- c("Stock Parameter", "Value")
StockDensityBySpecies_TP_Table <- kbl(StockDensityBySpecies_TP, caption = "Stocking Densities for TP Ponds") %>%
kable_paper(full_width = TRUE)
##############################################################
# Get the yield data
# This is corrected for mortality data & double harvest for tilapia
##############################################################
SurveyData[Pangasius.Stock.Mortality.during.Production == 0, PangMort := 1.00]
SurveyData[Pangasius.Stock.Mortality.during.Production == 1, PangMort := 0.95]
SurveyData[Pangasius.Stock.Mortality.during.Production == 2, PangMort := 0.85]
SurveyData[Pangasius.Stock.Mortality.during.Production == 3, PangMort := 0.4]
SurveyData[Tilapia.Stock.Mortality.during.Production == 0, TilaMort := 1]
SurveyData[Tilapia.Stock.Mortality.during.Production == 1, TilaMort := 0.95]
SurveyData[Tilapia.Stock.Mortality.during.Production == 2, TilaMort := 0.85]
SurveyData[Tilapia.Stock.Mortality.during.Production == 3, TilaMort := 0.4]
SurveyData[Carp.Stock.Mortality.during.Production == 0, CarpMort := 1]
SurveyData[Carp.Stock.Mortality.during.Production == 1, CarpMort := 0.95]
SurveyData[Carp.Stock.Mortality.during.Production == 2, CarpMort := 0.85]
SurveyData[Carp.Stock.Mortality.during.Production == 3, CarpMort := 0.4]
SurveyData[Other.Fish.Stock.Mortality.during.Production == 0, OtherMort := 1]
SurveyData[Other.Fish.Stock.Mortality.during.Production == 1, OtherMort := 0.95]
SurveyData[Other.Fish.Stock.Mortality.during.Production == 2, OtherMort := 0.85]
SurveyData[Other.Fish.Stock.Mortality.during.Production == 3, OtherMort := 0.4]
SurveyData[, prod_Tila := ((((SurveyData$Tilapia.Weight.at.Harvest..g. - SurveyData$Tilapia.Fingerlings.Weight..g.) * SurveyData$Number.of.Tilapia.Stocked) / (SurveyData$Pond.surface.area * 40.46)) * 0.01 * 2 * TilaMort )]
SurveyData[, prod_Pang := ((((SurveyData$Pangasius.Weight.at.Harvest..g. - SurveyData$Pangasius.Fingerlings.Weight..g.) * SurveyData$Number.of.Pangasius.Stocked) / (SurveyData$Pond.surface.area * 40.46))* 0.01 * PangMort)]
SurveyData[, prod_Carp := ((((SurveyData$Carp.Weight.at.Harvest..g. - SurveyData$Carp.Fingerlings.Weight..g.) * SurveyData$Number.of.Carp.Fish.Stocked) / (SurveyData$Pond.surface.area * 40.46))* 0.01 * CarpMort)]
SurveyData[, prod_Othe := ((((SurveyData$Other.Weight.at.Harvest..g. - SurveyData$Other.Fingerlings.Weight..g.) * SurveyData$Number.of.Other.Fish.Stocked) / (SurveyData$Pond.surface.area * 40.46))* 0.01 * OtherMort)]
SurveyData[, prod_Total_Overall := rowSums(.SD, na.rm = TRUE), .SDcols = c("prod_Tila", "prod_Pang", "prod_Carp", "prod_Othe")]
SurveyData[prod_Total_Overall == 0, prod_Total_Overall := NA]
SurveyData[, prod_Total_TPC := rowSums(.SD, na.rm = TRUE), .SDcols = c("prod_Tila", "prod_Pang", "prod_Carp")]
SurveyData[prod_Total_TPC == 0, prod_Total_TPC := NA]
Yields_DF <- data.frame(rbind(
c("Overall Mean Yield", SurveyData[, mean(prod_Total_Overall, na.rm = TRUE)]),
c("Overall Std Yield", SurveyData[, sd(prod_Total_Overall, na.rm = TRUE)]),
c("Overall Tilapia Mean Yield", SurveyData[, mean(prod_Tila, na.rm = TRUE)]),
c("Overall Tilapia Std Yield", SurveyData[, sd(prod_Tila, na.rm = TRUE)]),
c("Overall Pang Mean Yield", SurveyData[, mean(prod_Pang, na.rm = TRUE)]),
c("Overall Pang Std Yield", SurveyData[, sd(prod_Pang, na.rm = TRUE)]),
c("Overall Carp Mean Yield", SurveyData[, mean(prod_Carp, na.rm = TRUE)]),
c("Overall Carp Std Yield", SurveyData[, sd(prod_Carp, na.rm = TRUE)])
))
colnames(Yields_DF) <- c("Stock Parameter", "Value")
Yields_DF_Table <- kbl(Yields_DF, caption = "Overall Corrected Yield (tonnes/ha)") %>%
kable_paper(full_width = TRUE)
#######################################################
# TPC ponds yields
#######################################################
Yields_DF_TPC <- data.frame(rbind(
c("TPC: Overall Mean Yield", SurveyData[PolyCult == "TPC", mean(prod_Total_TPC, na.rm = TRUE)]),
c("TPC: Overall STD Yield", SurveyData[PolyCult == "TPC", sd(prod_Total_TPC, na.rm = TRUE)]),
c("TPC: Overall Range Yield", paste(SurveyData[PolyCult == "TPC", min(prod_Total_TPC, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(prod_Total_TPC, na.rm = TRUE)])),
c("TPC: Tilapia Mean Yield", SurveyData[PolyCult == "TPC", mean(prod_Tila, na.rm = TRUE)]),
c("TPC: Tilapia STD Yield", SurveyData[PolyCult == "TPC", sd(prod_Tila, na.rm = TRUE)]),
c("TPC: Tilapia Range Yield", paste(SurveyData[PolyCult == "TPC", min(prod_Tila, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", min(prod_Tila, na.rm = TRUE)])),
c("TPC: Pangasius Mean Yield", SurveyData[PolyCult == "TPC", mean(prod_Pang, na.rm = TRUE)]),
c("TPC: Pangasius STD Yield", SurveyData[PolyCult == "TPC", sd(prod_Pang, na.rm = TRUE)]),
c("TPC: Pangasius Range Yield", paste(SurveyData[PolyCult == "TPC", min(prod_Pang, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(prod_Pang, na.rm = TRUE)])),
c("TPC: Carp Mean Yield", SurveyData[PolyCult == "TPC", mean(prod_Carp, na.rm = TRUE)]),
c("TPC: Carp STD Yield", SurveyData[PolyCult == "TPC", sd(prod_Carp, na.rm = TRUE)]),
c("TPC: Carp Range Yield", paste(SurveyData[PolyCult == "TPC", min(prod_Carp, na.rm = TRUE)],":", SurveyData[PolyCult == "TPC", max(prod_Carp, na.rm = TRUE)]))))
colnames(Yields_DF_TPC) <- c("Stock Parameter", "Value")
Yields_DF_TPC_Table <- kbl(Yields_DF_TPC, caption = "Overall Corrected Yield for TPC Ponds (tonnes/ha)") %>%
kable_paper(full_width = TRUE)
Yields_DF_TC <- data.frame(rbind(
c("TC: Overall Mean Yield", SurveyData[PolyCult == "TC", mean(prod_Total_TPC, na.rm = TRUE)]),
c("TC: Overall STD Yield", SurveyData[PolyCult == "TC", sd(prod_Total_TPC, na.rm = TRUE)]),
c("TC: Overall Range Yield", paste(SurveyData[PolyCult == "TC", min(prod_Total_TPC, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(prod_Total_TPC, na.rm = TRUE)])),
c("TC: Tilapia Mean Yield", SurveyData[PolyCult == "TC", mean(prod_Tila, na.rm = TRUE)]),
c("TC: Tilapia STD Yield", SurveyData[PolyCult == "TC", sd(prod_Tila, na.rm = TRUE)]),
c("TC: Tilapia Range Yield", paste(SurveyData[PolyCult == "TC", min(prod_Tila, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", min(prod_Tila, na.rm = TRUE)])),
c("TC: Pangasius Mean Yield", SurveyData[PolyCult == "TC", mean(prod_Pang, na.rm = TRUE)]),
c("TC: Pangasius STD Yield", SurveyData[PolyCult == "TC", sd(prod_Pang, na.rm = TRUE)]),
c("TC: Pangasius Range Yield", paste(SurveyData[PolyCult == "TC", min(prod_Pang, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(prod_Pang, na.rm = TRUE)])),
c("TC: Carp Mean Yield", SurveyData[PolyCult == "TC", mean(prod_Carp, na.rm = TRUE)]),
c("TC: Carp STD Yield", SurveyData[PolyCult == "TC", sd(prod_Carp, na.rm = TRUE)]),
c("TC: Carp Range Yield", paste(SurveyData[PolyCult == "TC", min(prod_Carp, na.rm = TRUE)],":", SurveyData[PolyCult == "TC", max(prod_Carp, na.rm = TRUE)]))))## Warning in min(prod_Pang, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in max(prod_Pang, na.rm = TRUE): no non-missing arguments to max;
## returning -Infcolnames(Yields_DF_TC) <- c("Stock Parameter", "Value")
Yields_DF_TC_Table <- kbl(Yields_DF_TC, caption = "Overall Corrected Yield for TC Ponds (tonnes/ha)") %>%
kable_paper(full_width = TRUE)
Yields_DF_PC <- data.frame(rbind(
c("PC: Overall Mean Yield", SurveyData[PolyCult == "PC", mean(prod_Total_TPC, na.rm = TRUE)]),
c("PC: Overall STD Yield", SurveyData[PolyCult == "PC", sd(prod_Total_TPC, na.rm = TRUE)]),
c("PC: Overall Range Yield", paste(SurveyData[PolyCult == "PC", min(prod_Total_TPC, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(prod_Total_TPC, na.rm = TRUE)])),
c("PC: Tilapia Mean Yield", SurveyData[PolyCult == "PC", mean(prod_Tila, na.rm = TRUE)]),
c("PC: Tilapia STD Yield", SurveyData[PolyCult == "PC", sd(prod_Tila, na.rm = TRUE)]),
c("PC: Tilapia Range Yield", paste(SurveyData[PolyCult == "PC", min(prod_Tila, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", min(prod_Tila, na.rm = TRUE)])),
c("PC: Pangasius Mean Yield", SurveyData[PolyCult == "PC", mean(prod_Pang, na.rm = TRUE)]),
c("PC: Pangasius STD Yield", SurveyData[PolyCult == "PC", sd(prod_Pang, na.rm = TRUE)]),
c("PC: Pangasius Range Yield", paste(SurveyData[PolyCult == "PC", min(prod_Pang, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(prod_Pang, na.rm = TRUE)])),
c("PC: Carp Mean Yield", SurveyData[PolyCult == "PC", mean(prod_Carp, na.rm = TRUE)]),
c("PC: Carp STD Yield", SurveyData[PolyCult == "PC", sd(prod_Carp, na.rm = TRUE)]),
c("PC: Carp Range Yield", paste(SurveyData[PolyCult == "PC", min(prod_Carp, na.rm = TRUE)],":", SurveyData[PolyCult == "PC", max(prod_Carp, na.rm = TRUE)]))))## Warning in min(prod_Tila, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in min(prod_Tila, na.rm = TRUE): no non-missing arguments to min;
## returning Infcolnames(Yields_DF_PC) <- c("Stock Parameter", "Value")
Yields_DF_PC_Table <- kbl(Yields_DF_PC, caption = "Overall Corrected Yield for PC Ponds (tonnes/ha)") %>%
kable_paper(full_width = TRUE)
Yields_DF_TP <- data.frame(rbind(
c("TP: Overall Mean Yield", SurveyData[PolyCult == "TP", mean(prod_Total_TPC, na.rm = TRUE)]),
c("TP: Overall STD Yield", SurveyData[PolyCult == "TP", sd(prod_Total_TPC, na.rm = TRUE)]),
c("TP: Overall Range Yield", paste(SurveyData[PolyCult == "TP", min(prod_Total_TPC, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(prod_Total_TPC, na.rm = TRUE)])),
c("TP: Tilapia Mean Yield", SurveyData[PolyCult == "TP", mean(prod_Tila, na.rm = TRUE)]),
c("TP: Tilapia STD Yield", SurveyData[PolyCult == "TP", sd(prod_Tila, na.rm = TRUE)]),
c("TP: Tilapia Range Yield", paste(SurveyData[PolyCult == "TP", min(prod_Tila, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", min(prod_Tila, na.rm = TRUE)])),
c("TP: Pangasius Mean Yield", SurveyData[PolyCult == "TP", mean(prod_Pang, na.rm = TRUE)]),
c("TP: Pangasius STD Yield", SurveyData[PolyCult == "TP", sd(prod_Pang, na.rm = TRUE)]),
c("TP: Pangasius Range Yield", paste(SurveyData[PolyCult == "TP", min(prod_Pang, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(prod_Pang, na.rm = TRUE)])),
c("TP: Carp Mean Yield", SurveyData[PolyCult == "TP", mean(prod_Carp, na.rm = TRUE)]),
c("TP: Carp STD Yield", SurveyData[PolyCult == "TP", sd(prod_Carp, na.rm = TRUE)]),
c("TP: Carp Range Yield", paste(SurveyData[PolyCult == "TP", min(prod_Carp, na.rm = TRUE)],":", SurveyData[PolyCult == "TP", max(prod_Carp, na.rm = TRUE)]))))## Warning in min(prod_Carp, na.rm = TRUE): no non-missing arguments to min;
## returning Inf## Warning in max(prod_Carp, na.rm = TRUE): no non-missing arguments to max;
## returning -Infcolnames(Yields_DF_TP) <- c("Stock Parameter", "Value")
Yields_DF_TP_Table <- kbl(Yields_DF_TP, caption = "Overall Corrected Yield for TP Ponds (tonnes/ha)") %>%
kable_paper(full_width = TRUE)
##############################################################
# Farms and food used
##############################################################
# Get the data
feed_stock <- SurveyData$Feeding.Practices.in.the.Pond
f1 <- rep(NA, length(feed_stock))
own <- rep(0, length(feed_stock))
float <- rep(0, length(feed_stock))
sink <- rep(0, length(feed_stock))
# Extract 10 - Company Pellet and 100 - Company Floating
c1 <- mapply(bin_output, feed_stock, 2)
c2 <- mapply(bin_output, feed_stock, 3)
f1[which(c1 == 0 | c2 ==0)] <- 0
f1[which(c1 == 1)] <- 1
f1[which(c2 == 2)] <- 2
# Extract 10 - Own food - 1
f2 <- rep(NA, length(feed_stock))
c3 <- mapply(bin_output, feed_stock, 1)
f2[which(c3 ==0)] <- 0
f2[which(c3 ==1)] <- 1
# For economics sort out 3 vectors for own, sink and float
own[(which(c3 == 1))] <- 1
float[(which(c2 == 1))] <- 1
sink[(which(c1 == 1))] <- 1
# Correct for multiple uses
MULTI <- rowSums(cbind(own,float,sink))
own <- own/MULTI
float <- float/MULTI
sink <- sink/MULTI
# Input whether own, company floating or sinking used
SurveyData[, OwnFeed := apply(.SD, 1, bin_output, digit_index = 1), .SDcols = c("Feeding.Practices.in.the.Pond")]
SurveyData[, SinkFeed := apply(.SD, 1, bin_output, digit_index = 2), .SDcols = c("Feeding.Practices.in.the.Pond")]
SurveyData[, FloatFeed := apply(.SD, 1, bin_output, digit_index = 3), .SDcols = c("Feeding.Practices.in.the.Pond")]
# To compensate for multiple feed use assume equal use
SurveyData[, MultiFeeds := rowSums(.SD, na.rm = TRUE), .SDcols = c("OwnFeed", "SinkFeed", "FloatFeed")]
SurveyData[, OwnFeed := (OwnFeed/MultiFeeds)]
SurveyData[, SinkFeed := (SinkFeed/MultiFeeds)]
SurveyData[, FloatFeed := (FloatFeed/MultiFeeds)]| Parameter | Value |
|---|---|
| Mean age of all ponds | 12.4156378600823 |
| Age of all ponds STD | 4.34609189702699 |
| Mean age of all tilapia ponds | 12.0508474576271 |
| Age of all tilapia ponds STD | 4.36446287755471 |
| Mean age of all pang ponds | 13.0328947368421 |
| Age of all pang ponds STD | 4.6213122232049 |
| Mean age of all carp ponds | 12.3865546218487 |
| Age of all carp ponds STD | 4.3605621201094 |
| Mean age of Mymensingh district ponds | 12.3625730994152 |
| Age of Mymensingh district ponds STD | 4.21448515300732 |
| Mean age of all tilapia Mymensingh ponds | 11.9256198347107 |
| Age of all tilapia Mymensingh ponds STD | 4.24296533345921 |
| Mean age of all pang Mymensingh ponds | 13.3265306122449 |
| Age of all pang Mymensingh ponds STD | 4.39253234667418 |
| Mean age of all carp Mymensingh ponds | 12.3192771084337 |
| Age of all carp Mymensingh ponds STD | 4.2312533519994 |
| Stock Parameter | Value |
|---|---|
| Overall Mean Stocking Density per ha | 6.67720052848352 |
| Overall STD Stocking Density per ha | 4.24875104541016 |
| Overall Range Stocking Density per ha | 2.16262975778547 : 35.151315428132 |
| Tilapia Mean Stocking Density per ha | 3.01091889628701 |
| Tilapia STD Stocking Density per ha | 1.70546901210543 |
| Tilapia Range Stocking Density per ha | 0.192233756247597 : 7.72367770637667 |
| Pangasius Mean Stocking Density per ha | 4.94085431649446 |
| Pangasius STD Stocking Density per ha | 2.94489253250248 |
| Pangasius Range Stocking Density per ha | 0.823858955346845 : 28.51819460816 |
| Carp Mean Stocking Density per ha | 0.826753890295517 |
| Carp STD Stocking Density per ha | 0.958856149367322 |
| Carp Range Stocking Density per ha | 0.0549239303564563 : 8.98755224014739 |
| Stock Parameter | Value |
|---|---|
| TPC: Overall Mean Stocking Density | 8.03320832484454 |
| TPC: Overall STD Stocking Density | 4.99097593092944 |
| TPC: Overall Range Stocking Density | 2.30680507497117 : 30.8756986957679 |
| TPC: Tilapia Mean Stocking Density | 1.98223700015263 |
| TPC: Tilapia STD Stocking Density | 1.47794271473059 |
| TPC: Tilapia Range Stocking Density | 0.192233756247597 : 7.72367770637667 |
| TPC: Pangasius Mean Stocking Density | 4.67396336130976 |
| TPC: Pangasius STD Stocking Density | 3.55627688735698 |
| TPC: Pangasius Range Stocking Density | 0.823858955346845 : 28.51819460816 |
| TPC: Carp Mean Stocking Density | 1.03688220824484 |
| TPC: Carp STD Stocking Density | 1.27165665472187 |
| TPC: Carp Range Stocking Density | 0.0882706023585905 : 8.98755224014739 |
| Stock Parameter | Value |
|---|---|
| TC: Overall Mean Stocking Density | 5.19266904261417 |
| TC: Overall STD Stocking Density | 2.80987959511493 |
| TC: Overall Range Stocking Density | 2.47157686604053 : 25.0247157686604 |
| TC: Tilapia Mean Stocking Density | 3.9962866102314 |
| TC: Tilapia STD Stocking Density | 1.27672608180876 |
| TC: Tilapia Range Stocking Density | 2.00815620365793 : 7.32319071419417 |
| TC: Pangasius Mean Stocking Density | NaN |
| TC: Pangasius STD Stocking Density | NA |
| TC: Pangasius Range Stocking Density | Inf : -Inf |
| TC: Carp Mean Stocking Density | 0.603336552104346 |
| TC: Carp STD Stocking Density | 0.52830288224891 |
| TC: Carp Range Stocking Density | 0.0549239303564563 : 2.47157686604053 |
| Stock Parameter | Value |
|---|---|
| PC: Overall Mean Stocking Density | 6.96743315822406 |
| PC: Overall STD Stocking Density | 4.41869399123186 |
| PC: Overall Range Stocking Density | 2.16262975778547 : 35.151315428132 |
| PC: Tilapia Mean Stocking Density | NaN |
| PC: Tilapia STD Stocking Density | NA |
| PC: Tilapia Range Stocking Density | Inf : -Inf |
| PC: Pangasius Mean Stocking Density | 5.17639101745513 |
| PC: Pangasius STD Stocking Density | 2.00781923976017 |
| PC: Pangasius Range Stocking Density | 1.8536826495304 : 10.2982369418356 |
| PC: Carp Mean Stocking Density | 0.876914254257665 |
| PC: Carp STD Stocking Density | 0.920919160948693 |
| PC: Carp Range Stocking Density | 0.133465150766189 : 3.95452298566485 |
| Stock Parameter | Value |
|---|---|
| TP: Overall Mean Stocking Density | 7.8972765576819 |
| TP: Overall STD Stocking Density | 2.89717988642649 |
| TP: Overall Range Stocking Density | 4.32525951557093 : 11.2986371018996 |
| TP: Tilapia Mean Stocking Density | 1.74187321987619 |
| TP: Tilapia STD Stocking Density | 1.44466256574069 |
| TP: Tilapia Range Stocking Density | 0.617894216510133 : 4.23698891321234 |
| TP: Pangasius Mean Stocking Density | 6.15540333780571 |
| TP: Pangasius STD Stocking Density | 2.1747952979548 |
| TP: Pangasius Range Stocking Density | 3.7073652990608 : 8.82706023585905 |
| TP: Carp Mean Stocking Density | NaN |
| TP: Carp STD Stocking Density | NA |
| TP: Carp Range Stocking Density | Inf : -Inf |
The yields are corrected for reported mortality within the ponds. The means yields are:
| Stock Parameter | Value |
|---|---|
| Overall Mean Yield | 54.3595368996059 |
| Overall Std Yield | 42.1170915285572 |
| Overall Tilapia Mean Yield | 18.7489372364852 |
| Overall Tilapia Std Yield | 13.4269019895518 |
| Overall Pang Mean Yield | 57.6605546465429 |
| Overall Pang Std Yield | 37.9369522055356 |
| Overall Carp Mean Yield | 6.47879239274016 |
| Overall Carp Std Yield | 10.1569078715783 |
For TPC ponds:
| Stock Parameter | Value |
|---|---|
| TPC: Overall Mean Yield | 73.8955788907229 |
| TPC: Overall STD Yield | 54.5806503395738 |
| TPC: Overall Range Yield | 3.05585763717252 : 326.276664511959 |
| TPC: Tilapia Mean Yield | 12.0388890481929 |
| TPC: Tilapia STD Yield | 9.01521546402402 |
| TPC: Tilapia Range Yield | 1.08131487889273 : 1.08131487889273 |
| TPC: Pangasius Mean Yield | 55.7782178489327 |
| TPC: Pangasius STD Yield | 44.9890557636017 |
| TPC: Pangasius Range Yield | 6.07595979568298 : 311.561276094148 |
| TPC: Carp Mean Yield | 8.63766629558377 |
| TPC: Carp STD Yield | 15.7598548060328 |
| TPC: Carp Range Yield | 0.74147305981216 : 125.196602705253 |
For TC ponds:
| Stock Parameter | Value |
|---|---|
| TC: Overall Mean Yield | 28.3550266116611 |
| TC: Overall STD Yield | 15.0806592853851 |
| TC: Overall Range Yield | 3.97217710613657 : 101.396440929313 |
| TC: Tilapia Mean Yield | 25.6112109200145 |
| TC: Tilapia STD Yield | 13.6863241188272 |
| TC: Tilapia Range Yield | 6.0306475531389 : 6.0306475531389 |
| TC: Pangasius Mean Yield | NaN |
| TC: Pangasius STD Yield | NA |
| TC: Pangasius Range Yield | Inf : -Inf |
| TC: Carp Mean Yield | 4.53970306288563 |
| TC: Carp STD Yield | 3.81501828825807 |
| TC: Carp Range Yield | 0.384467512495194 : 19.7726149283243 |
For PC ponds:
| Stock Parameter | Value |
|---|---|
| PC: Overall Mean Yield | 64.0512428840126 |
| PC: Overall STD Yield | 31.1172718129203 |
| PC: Overall Range Yield | 7.00692041522491 : 159.107760751359 |
| PC: Tilapia Mean Yield | NaN |
| PC: Tilapia STD Yield | NA |
| PC: Tilapia Range Yield | Inf : Inf |
| PC: Pangasius Mean Yield | 59.0013551190321 |
| PC: Pangasius STD Yield | 28.5355589280026 |
| PC: Pangasius Range Yield | 17.6099851705388 : 141.640366553861 |
| PC: Carp Mean Yield | 6.53823246012901 |
| PC: Carp STD Yield | 6.53046688539683 |
| PC: Carp Range Yield | 0.906244850881529 : 30.5857637172516 |
For TP ponds:
| Stock Parameter | Value |
|---|---|
| TP: Overall Mean Yield | 77.734093637455 |
| TP: Overall STD Yield | 28.7871287269589 |
| TP: Overall Range Yield | 30.7093425605536 : 105.147941529553 |
| TP: Tilapia Mean Yield | 8.13949109055387 |
| TP: Tilapia STD Yield | 4.77263621573318 |
| TP: Tilapia Range Yield | 2.34799802273851 : 2.34799802273851 |
| TP: Pangasius Mean Yield | 69.5946025469011 |
| TP: Pangasius STD Yield | 24.9795860084782 |
| TP: Pangasius Range Yield | 28.3613445378151 : 89.8947814419885 |
| TP: Carp Mean Yield | NaN |
| TP: Carp STD Yield | NA |
| TP: Carp Range Yield | Inf : -Inf |
In this section the data is used to work out the economics of the finfish aquaculture and assess the impact of different types of polyculture. In particular, this focusses on tilapia, pangasius and the inclusion of carp - often used a maintain pond water quality by using the primary production from the pond and feeding from the bottom.
###################################################
#
# Now add the economics to this
#
# Price of fingerlings
# Value of harvest
# Cost of feed
###################################################
# Set the conversion rate to dollar
Conversion = 1/80
# Read in the feed cost look up table
FeedCostDF <- read.csv("FeedPrices.csv", header = TRUE)
# Calculate the feed needed per harvested fish using the FCR and harvest weight
SurveyData[, Feed_Tila := (((SurveyData$Tilapia.Weight.at.Harvest..g. - SurveyData$Tilapia.Fingerlings.Weight..g.)/1000) * SurveyData$Food.Conversion.Ratio.of.the.Pond)]
SurveyData[, Feed_Pang := (((SurveyData$Pangasius.Weight.at.Harvest..g. - SurveyData$Pangasius.Fingerlings.Weight..g.)/1000) * SurveyData$Food.Conversion.Ratio.of.the.Pond)]
SurveyData[, Feed_Carp := (((SurveyData$Carp.Weight.at.Harvest..g. - SurveyData$Carp.Fingerlings.Weight..g.)/1000) * SurveyData$Food.Conversion.Ratio.of.the.Pond)]
# Calculate the total feed required
SurveyData[, Total_Feed_Needed := rowSums(.SD, na.rm = TRUE), .SDcols = c("Feed_Tila", "Feed_Pang", "Feed_Carp")]
##################################################################
# Get the feed that is used by carp from Pang/Tilapia feed
##################################################################
# Here using the same approach as above but with estimates of the amounts of feed used by carp
# Best case is when carp use just the primary productivity and waste from other feed
# Worst case is when carp need feeding
# Realistic case is using the proportions of carp species to average this
SurveyData[, Carp_Feed_Best := 0]
# For worst case grass/mirror use 70% feed (sinking) & 80% feed (floating)
SurveyData[, Carp_Feed_Worst := ((Feed_Carp * 0.7 * OwnFeed) + (Feed_Carp * 0.7 * SinkFeed) + (Feed_Carp * 0.7 * FloatFeed))]
# Now realistic based on proportion of carp produced in Mymensingh
# Put in values to correct for food obtained from the pond by specific species by feeding group
GrassMirror_2018 <- 0.333 # 33.3% of productivity in 2018 in Mymensingh was grass (11.4) and mirror (6.42) & other (15.5)
GrassMirror_Sink <- 0.7 * GrassMirror_2018 # Gets 30% of food from pond
GrassMirror_Float <- 0.8 * GrassMirror_2018 # Gets 20% of food from pond
RuiGonia_2018 <- 0.241 # 24.1% of productivity in 2018 in Mymensingh was rui (19.1) & gonia (4.99)
RuiGonia_Sink <- 0.5 * RuiGonia_2018 # Gets 50% of food from pond
RuiGonia_Float <- 0.5 * RuiGonia_2018 # Gets 50% of food from pond
BataSilver_2018 <- 0.250 # 24.98% of productivity in 2018 in Mymensingh was bata (7.48) & silver (17.5)
BataSilver_Sink <- 0 # Gets 100% of food from pond
BataSilver_Float <- 0 # Gets 100% of food from pond
Mrigal_2018 <- 0.176 # 17.6% of productivity in 2018 in Mymensingh was from Mrigal
Mrigal_Sink <- 0.5 * Mrigal_2018 # Gets 50% of food from pond
Mrigal_Float <- 0.2 * Mrigal_2018 # Gets 80% of food from pond
Real_Carp_Sink <- GrassMirror_Sink + RuiGonia_Sink + BataSilver_Sink + Mrigal_Sink
Real_Carp_Float <- GrassMirror_Float + RuiGonia_Float + BataSilver_Float + Mrigal_Float
SurveyData[, Carp_Feed_Real := ((Feed_Carp * Real_Carp_Sink * OwnFeed) + (Feed_Carp * Real_Carp_Sink * SinkFeed) + (Feed_Carp * Real_Carp_Float * FloatFeed))]
# Calculate the cost based on own and company feed for 1 fish in each pond
Cost_own_feed <- FeedCostDF[5, 9] # estimate
SurveyData[, Cost_Tila := -((Feed_Tila * Cost_own_feed * own) + (Feed_Tila * FeedCostDF[4, 9] * float) + (Feed_Tila * FeedCostDF[3, 9] * sink))]
SurveyData[, Cost_Pang := -((Feed_Pang * Cost_own_feed * own) + (Feed_Pang * FeedCostDF[2, 9] * float) + (Feed_Pang * FeedCostDF[1, 9] * sink))]
SurveyData[, Cost_Carp_Best := -((Carp_Feed_Best * Cost_own_feed * own) + (Carp_Feed_Best * FeedCostDF[7, 9] * float) + (Carp_Feed_Best * FeedCostDF[6, 9] * sink))]
SurveyData[, Cost_Carp_Worst := -((Carp_Feed_Worst * Cost_own_feed * own) + (Carp_Feed_Worst * FeedCostDF[7, 9] * float) + (Carp_Feed_Worst * FeedCostDF[6, 9] * sink))]
SurveyData[, Cost_Carp_Real := -((Carp_Feed_Real * Cost_own_feed * own) + (Carp_Feed_Real * FeedCostDF[7, 9] * float) + (Carp_Feed_Real * FeedCostDF[6, 9] * sink))]
##########################################################
# Adjust cost by number of fish and adjust for mort level
################################################################
SurveyData[, Cost_Tila := (Cost_Tila * Number.of.Tilapia.Stocked * TilaMort * Conversion)]
SurveyData[, Cost_Pang := (Cost_Pang * Number.of.Pangasius.Stocked * PangMort * Conversion)]
SurveyData[, Cost_Carp_Best := (Cost_Carp_Best * Number.of.Carp.Fish.Stocked * CarpMort * Conversion)]
SurveyData[, Cost_Carp_Worst := (Cost_Carp_Worst * Number.of.Carp.Fish.Stocked * CarpMort * Conversion)]
SurveyData[, Cost_Carp_Real := (Cost_Carp_Real * Number.of.Carp.Fish.Stocked * CarpMort * Conversion)]
# Adjust the total to be cost per ha farm
Dec2Hec <- 0.004046
SurveyData[, Cost_Tila_Hectare := (Cost_Tila/(Pond.surface.area * Dec2Hec))]
SurveyData[, Cost_Pang_Hectare := (Cost_Pang/(Pond.surface.area * Dec2Hec))]
SurveyData[, Cost_Carp_Hectare_Best := (Cost_Carp_Best/(Pond.surface.area * Dec2Hec))]
SurveyData[, Cost_Carp_Hectare_Worst := (Cost_Carp_Worst/(Pond.surface.area * Dec2Hec))]
SurveyData[, Cost_Carp_Hectare_Real := (Cost_Carp_Real/(Pond.surface.area * Dec2Hec))]
# Get the overall feed costs
SurveyData[, Cost_Overall_Hectare := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Tila_Hectare", "Cost_Pang_Hectare", "Cost_Carp_Hectare_Real")]
SurveyData[Cost_Overall_Hectare == 0, Cost_Overall_Hectare := NA]
#################################################
# Cost of fingerlings
#################################################
# Read in the cost data
FingerlingCostDF <- read.csv("FingerlingPrices.csv", header = TRUE)
SurveyData[, Finger_Cost_Tila := -(Number.of.Tilapia.Stocked * FingerlingCostDF[1,2] * Conversion)]
SurveyData[, Finger_Cost_Pang := -(Number.of.Pangasius.Stocked * FingerlingCostDF[2,2] * Conversion)]
SurveyData[, Finger_Cost_Carp := -(Number.of.Carp.Fish.Stocked * FingerlingCostDF[3,2] * Conversion)]
SurveyData[, Finger_Cost_Tila_Hectare := (Finger_Cost_Tila/(Pond.surface.area * Dec2Hec))]
SurveyData[, Finger_Cost_Pang_Hectare := (Finger_Cost_Pang/(Pond.surface.area * Dec2Hec))]
SurveyData[, Finger_Cost_Carp_Hectare := (Finger_Cost_Carp/(Pond.surface.area * Dec2Hec))]
#################################################
# Get the harvest value - take 2019 prices
#
# In this section this is PER HARVEST CYCLE
#################################################
FarmGatePrice <- read.csv("FarmGatePrices.csv", header = TRUE)
SurveyData[, Harvest_Tila := (((Tilapia.Weight.at.Harvest..g. * Number.of.Tilapia.Stocked)/1000) * TilaMort * FarmGatePrice[1,5] * Conversion)]
SurveyData[, Harvest_Pang := (((Pangasius.Weight.at.Harvest..g. * Number.of.Pangasius.Stocked)/1000) * PangMort * FarmGatePrice[2,5] * Conversion)]
SurveyData[, Harvest_Carp := (((Carp.Weight.at.Harvest..g. * Number.of.Carp.Fish.Stocked)/1000) * CarpMort * FarmGatePrice[3,5] * Conversion)]
SurveyData[, Harvest_Tila_Hectare := (Harvest_Tila/(Pond.surface.area * Dec2Hec))]
SurveyData[, Harvest_Pang_Hectare := (Harvest_Pang/(Pond.surface.area * Dec2Hec))]
SurveyData[, Harvest_Carp_Hectare := (Harvest_Carp/(Pond.surface.area * Dec2Hec))]
SurveyData[, Harvest_Overall := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Tila", "Harvest_Pang", "Harvest_Carp")]
SurveyData[, Harvest_Overall_Hectare := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Tila_Hectare", "Harvest_Pang_Hectare", "Harvest_Carp_Hectare")]
###########################################################
# Work out the additional costs
#
# Assume flat fee for carps as cultured with pang or tila
###########################################################
AddCosts <- read.csv("AdditionalCosts.csv", header = TRUE)
Tila_AddCost <- sum(AddCosts[ ,2])
Pang_AddCost <- sum(AddCosts[ ,3])
Carp_AddCost <- sum(AddCosts[ ,3]) * 0.5
# Split the additional costs for pang and tilapia farming 50:50 if both present
SurveyData[PolyCult == "TC", AddCost_Tila := -(Pond.surface.area * Dec2Hec * Tila_AddCost * 1 * Conversion)]
SurveyData[PolyCult == "TC", AddCost_Pang := 0]
SurveyData[PolyCult == "TC", AddCost_Carp := -(Pond.surface.area * Dec2Hec * Carp_AddCost * 1 * Conversion)]
SurveyData[PolyCult == "PC", AddCost_Pang := -(Pond.surface.area * Dec2Hec * Pang_AddCost * 1 * Conversion)]
SurveyData[PolyCult == "PC", AddCost_Tila := 0]
SurveyData[PolyCult == "PC", AddCost_Carp := -(Pond.surface.area * Dec2Hec * Carp_AddCost * 1 * Conversion)]
SurveyData[PolyCult == "TP", AddCost_Tila := -(Pond.surface.area * Dec2Hec * Tila_AddCost * 0.5 * Conversion)]
SurveyData[PolyCult == "TP", AddCost_Pang := -(Pond.surface.area * Dec2Hec * Pang_AddCost * 0.5 * Conversion)]
SurveyData[PolyCult == "TP", AddCost_Carp := 0]
SurveyData[PolyCult == "TPC", AddCost_Tila := -(Pond.surface.area * Dec2Hec * Tila_AddCost * 0.5 * Conversion)]
SurveyData[PolyCult == "TPC", AddCost_Pang := -(Pond.surface.area * Dec2Hec * Pang_AddCost * 0.5 * Conversion)]
SurveyData[PolyCult == "TPC", AddCost_Carp := -(Pond.surface.area * Dec2Hec * Carp_AddCost * 1 * Conversion)]
#################################################
# Work out total costs
#################################################
SurveyData[, total_cost_Tila := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Tila", "Finger_Cost_Tila", "AddCost_Tila")]
SurveyData[, total_cost_Pang := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Pang", "Finger_Cost_Pang", "AddCost_Pang")]
SurveyData[, total_cost_Carp_Real := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Carp_Real", "Finger_Cost_Carp", "AddCost_Carp")]
SurveyData[, total_cost_Carp_Best := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Carp_Best", "Finger_Cost_Carp", "AddCost_Carp")]
SurveyData[, total_cost_Carp_Worst := rowSums(.SD, na.rm = TRUE), .SDcols = c("Cost_Carp_Worst", "Finger_Cost_Carp", "AddCost_Carp")]
SurveyData[, total_cost_Tila_hectare := (total_cost_Tila/(Pond.surface.area * Dec2Hec))]
SurveyData[, total_cost_Pang_hectare := (total_cost_Pang/(Pond.surface.area * Dec2Hec))]
SurveyData[, total_cost_Carp_hectare := (total_cost_Carp_Real/(Pond.surface.area * Dec2Hec))]
SurveyData[, Overall_TotalCost := rowSums(.SD, na.rm = TRUE), .SDcols = c("total_cost_Tila_hectare","total_cost_Pang_hectare", "total_cost_Carp_hectare")]
SurveyData[Overall_TotalCost == 0, Overall_TotalCost := NA]
#################################################
# Work out profit
#################################################
SurveyData[, profit_Tila := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Tila", "total_cost_Tila")]
SurveyData[, profit_Pang := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Pang", "total_cost_Pang")]
SurveyData[, profit_Carp_Real := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Carp", "total_cost_Carp_Real")]
###################################################
# Benefit cost ratio - separately
###################################################
SurveyData[, BCR_tila := (Harvest_Tila/-total_cost_Tila)]
SurveyData[, BCR_pang := (Harvest_Pang/-total_cost_Pang)]
SurveyData[, BCR_Carp := (Harvest_Carp/-total_cost_Carp_Real)]
####################################################
# BCR overall
#####################################################
SurveyData[, Overall_BCR := (Harvest_Overall_Hectare/-Overall_TotalCost)]
################################################
# Scale to per fish
###############################################
SurveyData[, profit_Per_Tila := (profit_Tila / Number.of.Tilapia.Stocked)]
SurveyData[, profit_Per_Pang := (profit_Pang / Number.of.Pangasius.Stocked)]
SurveyData[, profit_Per_Carp := (profit_Carp_Real / Number.of.Carp.Fish.Stocked )]
###################################################################
# Output the data for reporting
###################################################################
knitr::kable(data.frame(rbind(
c("Mean Harvest revenue per ha: overall", SurveyData[, mean(Harvest_Overall_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: tilapia", SurveyData[, mean(Harvest_Tila_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: pangasius", SurveyData[, mean(Harvest_Pang_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: carp", SurveyData[, mean(Harvest_Carp_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: overall", SurveyData[, mean(Cost_Overall_Hectare, na.rm = TRUE)]),
c("Mean Feed cost: tilapia", SurveyData[, mean(Cost_Tila_Hectare, na.rm = TRUE)]),
c("Mean Feed cost: pangasius", SurveyData[, mean(Cost_Pang_Hectare, na.rm = TRUE)]),
c("Mean Feed cost: carp", SurveyData[, mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]),
c("Mean BCR: tilapia", SurveyData[, mean(BCR_tila, na.rm = TRUE)]),
c("Mean BCR: pangasius", SurveyData[, mean(BCR_pang, na.rm = TRUE)]),
c("Mean BCR: carp", SurveyData[, mean(BCR_Carp, na.rm = TRUE)]),
c("Mean BCR: overall", SurveyData[, mean(Overall_BCR, na.rm = TRUE)]),
c("Mean Profit per tilapia", SurveyData[, mean(profit_Per_Tila, na.rm = TRUE)]),
c("Mean Profit per pangasius", SurveyData[, mean(profit_Per_Pang, na.rm = TRUE)]),
c("Mean Profit per carp", SurveyData[, mean(profit_Per_Carp, na.rm = TRUE)])
))
, caption = "Economic Factors in Pond Practice")| X1 | X2 |
|---|---|
| Mean Harvest revenue per ha: overall | 69707.2642373243 |
| Mean Harvest revenue per ha: tilapia | 13173.0217131139 |
| Mean Harvest revenue per ha: pangasius | 80947.1199106026 |
| Mean Harvest revenue per ha: carp | 10647.5146294209 |
| Mean Feed cost per ha: overall | -38624.5909396683 |
| Mean Feed cost: tilapia | -8377.03620979278 |
| Mean Feed cost: pangasius | -49729.4507247077 |
| Mean Feed cost: carp | -2387.74799639367 |
| Mean BCR: tilapia | 1.4405205337782 |
| Mean BCR: pangasius | 2.06064169832996 |
| Mean BCR: carp | 3.49306092209322 |
| Mean BCR: overall | 1.82880563877581 |
| Mean Profit per tilapia | 0.0763523411308361 |
| Mean Profit per pangasius | 0.584344923310904 |
| Mean Profit per carp | 0.817145835200413 |
#############################################
# Split economics according to polyculture
#################################################################################################
#
# In this section we assume 2 tilapia harvests for 1 pangasius and 1 carp per production cycle
#
#################################################################################################
PangProductionCycle <- 1
TilaProductionCycle <- 2
CarpProductionCycle <- 1
# Work out harvest revenue per production cycle
SurveyData[, Harvest_Tila_Hectare_PC := Harvest_Tila_Hectare * TilaProductionCycle]
SurveyData[, Harvest_Pang_Hectare_PC := Harvest_Pang_Hectare * PangProductionCycle]
SurveyData[, Harvest_Carp_Hectare_PC := Harvest_Carp_Hectare * CarpProductionCycle]
SurveyData[, Overall_Harvest_Hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Tila_Hectare_PC","Harvest_Pang_Hectare_PC","Harvest_Carp_Hectare_PC")]
# Work out the total costs per production cycle
SurveyData[, total_cost_Tila_hectare_PC := total_cost_Tila_hectare * TilaProductionCycle]
SurveyData[, total_cost_Pang_hectare_PC := total_cost_Pang_hectare * PangProductionCycle]
SurveyData[, total_cost_Carp_hectare_PC := total_cost_Carp_hectare * CarpProductionCycle]
SurveyData[, Overall_Cost_Hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("total_cost_Tila_hectare_PC","total_cost_Pang_hectare_PC","total_cost_Carp_hectare_PC")]
# Work out the total profit per production cycle
SurveyData[, profit_Tila_hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Tila_Hectare_PC", "total_cost_Tila_hectare_PC")]
SurveyData[, profit_Pang_hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Pang_Hectare_PC", "total_cost_Pang_hectare_PC")]
SurveyData[, profit_Carp_hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("Harvest_Carp_Hectare_PC", "total_cost_Carp_hectare_PC")]
SurveyData[, Overall_profit_hectare_PC := rowSums(.SD, na.rm = TRUE), .SDcols = c("Overall_Harvest_Hectare_PC", "Overall_Cost_Hectare_PC")]
knitr::kable(data.frame(rbind(
c("Mean Harvest revenue per ha: overall TP Ponds", SurveyData[PolyCult == "TP", mean(Overall_Harvest_Hectare_PC, na.rm = TRUE)]),
c("Sample size for Harvest revenue: overall TP Ponds", SurveyData[PolyCult == "TP" & !is.na(Overall_Harvest_Hectare_PC), .N]),
c("Mean Harvest revenue per ha: overall TC Ponds", SurveyData[PolyCult == "TC", mean(Overall_Harvest_Hectare_PC, na.rm = TRUE)]),
c("Sample size for Harvest revenue: overall TC Ponds", SurveyData[PolyCult == "TC" & !is.na(Overall_Harvest_Hectare_PC), .N]),
c("Mean Harvest revenue per ha: overall PC Ponds", SurveyData[PolyCult == "PC", mean(Overall_Harvest_Hectare_PC, na.rm = TRUE)]),
c("Sample size for Harvest revenue: overall PC Ponds", SurveyData[PolyCult == "PC" & !is.na(Overall_Harvest_Hectare_PC), .N]),
c("Mean Harvest revenue per ha: overall TPC Ponds", SurveyData[PolyCult == "TPC", mean(Overall_Harvest_Hectare_PC, na.rm = TRUE)]),
c("Sample size for Harvest revenue: overall TPC Ponds", SurveyData[PolyCult == "TPC" & !is.na(Overall_Harvest_Hectare_PC), .N]),
c("Mean Harvest revenue per ha: tilapia in TP ponds", SurveyData[PolyCult == "TP", mean(Harvest_Tila_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: tilapia in TC ponds", SurveyData[PolyCult == "TC", mean(Harvest_Tila_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: tilapia in PC ponds", SurveyData[PolyCult == "PC", mean(Harvest_Tila_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: tilapia in TPC ponds", SurveyData[PolyCult == "TPC", mean(Harvest_Tila_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: pangasius in TP ponds", SurveyData[PolyCult == "TP", mean(Harvest_Pang_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: pangasius in TC ponds", SurveyData[PolyCult == "TC", mean(Harvest_Pang_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: pangasius in PC ponds", SurveyData[PolyCult == "PC", mean(Harvest_Pang_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: pangasius in TPC ponds", SurveyData[PolyCult == "TPC", mean(Harvest_Pang_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: carp in TP ponds", SurveyData[PolyCult == "TP", mean(Harvest_Carp_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: carp in TC ponds", SurveyData[PolyCult == "TC", mean(Harvest_Carp_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: carp in PC ponds", SurveyData[PolyCult == "PC", mean(Harvest_Carp_Hectare, na.rm = TRUE)]),
c("Mean Harvest revenue per ha: carp in TPC ponds", SurveyData[PolyCult == "TPC", mean(Harvest_Carp_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: overall in TP ponds", SurveyData[PolyCult == "TP", mean(Cost_Overall_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: overall in TC ponds", SurveyData[PolyCult == "TC", mean(Cost_Overall_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: overall in PC ponds", SurveyData[PolyCult == "PC", mean(Cost_Overall_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: overall in TPC ponds", SurveyData[PolyCult == "TPC", mean(Cost_Overall_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: tilapia in TP ponds", SurveyData[PolyCult == "TP", mean(Cost_Tila_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: tilapia in TC ponds", SurveyData[PolyCult == "TC", mean(Cost_Tila_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: tilapia in PC ponds", SurveyData[PolyCult == "PC", mean(Cost_Tila_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: tilapia in TPC ponds", SurveyData[PolyCult == "TPC", mean(Cost_Tila_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: pangasius in TP ponds", SurveyData[PolyCult == "TP", mean(Cost_Pang_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: pangasius in TC ponds", SurveyData[PolyCult == "TC", mean(Cost_Pang_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: pangasius in PC ponds", SurveyData[PolyCult == "PC", mean(Cost_Pang_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: pangasius in TPC ponds", SurveyData[PolyCult == "TPC", mean(Cost_Pang_Hectare, na.rm = TRUE)]),
c("Mean Feed cost per ha: carp in TP ponds", SurveyData[PolyCult == "TP", mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]),
c("Mean Feed cost per ha: carp in TC ponds", SurveyData[PolyCult == "TC", mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]),
c("Mean Feed cost per ha: carp in PC ponds", SurveyData[PolyCult == "PC", mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]),
c("Mean Feed cost per ha: carp in TPC ponds", SurveyData[PolyCult == "TPC", mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]),
c("Mean total cost per ha: in TP ponds", SurveyData[PolyCult == "TP", mean(Overall_TotalCost, na.rm = TRUE)]),
c("Mean total cost per ha: in TC ponds", SurveyData[PolyCult == "TC", mean(Overall_TotalCost, na.rm = TRUE)]),
c("Mean total cost per ha: in PC ponds", SurveyData[PolyCult == "PC", mean(Overall_TotalCost, na.rm = TRUE)]),
c("Mean total cost per ha: in TPC ponds", SurveyData[PolyCult == "TPC", mean(Overall_TotalCost, na.rm = TRUE)]),
c("Mean total cost per ha: Overall", SurveyData[, mean(Overall_TotalCost, na.rm = TRUE)]),
c("Mean BCR: tilapia in TP ponds", SurveyData[PolyCult == "TP", mean(BCR_tila, na.rm = TRUE)]),
c("Mean BCR: tilapia in TC ponds", SurveyData[PolyCult == "TC", mean(BCR_tila, na.rm = TRUE)]),
c("Mean BCR: tilapia in PC ponds", SurveyData[PolyCult == "PC", mean(BCR_tila, na.rm = TRUE)]),
c("Mean BCR: tilapia in TPC ponds", SurveyData[PolyCult == "TPC", mean(BCR_tila, na.rm = TRUE)]),
c("Mean BCR: pangasius in TP ponds", SurveyData[PolyCult == "TP", mean(BCR_pang, na.rm = TRUE)]),
c("Mean BCR: pangasius in TC ponds", SurveyData[PolyCult == "TC", mean(BCR_pang, na.rm = TRUE)]),
c("Mean BCR: pangasius in PC ponds", SurveyData[PolyCult == "PC", mean(BCR_pang, na.rm = TRUE)]),
c("Mean BCR: pangasius in TPC ponds", SurveyData[PolyCult == "TPC", mean(BCR_pang, na.rm = TRUE)]),
c("Mean BCR: carp in TP ponds", SurveyData[PolyCult == "TP", mean(BCR_Carp, na.rm = TRUE)]),
c("Mean BCR: carp in TC ponds", SurveyData[PolyCult == "TC", mean(BCR_Carp, na.rm = TRUE)]),
c("Mean BCR: carp in PC ponds", SurveyData[PolyCult == "PC", mean(BCR_Carp, na.rm = TRUE)]),
c("Mean BCR: carp in TPC ponds", SurveyData[PolyCult == "TPC", mean(BCR_Carp, na.rm = TRUE)]),
c("Mean BCR: overall in TP ponds", SurveyData[PolyCult == "TP", mean(Overall_BCR, na.rm = TRUE)]),
c("Mean BCR: overall in TC ponds", SurveyData[PolyCult == "TC", mean(Overall_BCR, na.rm = TRUE)]),
c("Mean BCR: overall in PC ponds", SurveyData[PolyCult == "PC", mean(Overall_BCR, na.rm = TRUE)]),
c("Mean BCR: overall in TPC ponds", SurveyData[PolyCult == "TPC", mean(Overall_BCR, na.rm = TRUE)]),
c("Mean Profit per tilapia in TP ponds", SurveyData[PolyCult == "TP", mean(profit_Per_Tila, na.rm = TRUE)]),
c("Mean Profit per tilapia in TC ponds", SurveyData[PolyCult == "TC", mean(profit_Per_Tila, na.rm = TRUE)]),
c("Mean Profit per tilapia in PC ponds", SurveyData[PolyCult == "PC", mean(profit_Per_Tila, na.rm = TRUE)]),
c("Mean Profit per tilapia in TPC ponds", SurveyData[PolyCult == "TPC", mean(profit_Per_Tila, na.rm = TRUE)]),
c("Mean Profit per pangasius in TP ponds", SurveyData[PolyCult == "TP", mean(profit_Per_Pang, na.rm = TRUE)]),
c("Mean Profit per pangasius in TC ponds", SurveyData[PolyCult == "TC", mean(profit_Per_Pang, na.rm = TRUE)]),
c("Mean Profit per pangasius in PC ponds", SurveyData[PolyCult == "PC", mean(profit_Per_Pang, na.rm = TRUE)]),
c("Mean Profit per pangasius in TPC ponds", SurveyData[PolyCult == "TPC", mean(profit_Per_Pang, na.rm = TRUE)]),
c("Mean Profit per carp in TP ponds", SurveyData[PolyCult == "TP", mean(profit_Per_Carp, na.rm = TRUE)]),
c("Mean Profit per carp in TC ponds", SurveyData[PolyCult == "TC", mean(profit_Per_Carp, na.rm = TRUE)]),
c("Mean Profit per carp in PC ponds", SurveyData[PolyCult == "PC", mean(profit_Per_Carp, na.rm = TRUE)]),
c("Mean Profit per carp in TPC ponds", SurveyData[PolyCult == "TPC", mean(profit_Per_Carp, na.rm = TRUE)])
))
, caption = "Economic Factors in Pond Practice by Polyculture type")| X1 | X2 |
|---|---|
| Mean Harvest revenue per ha: overall TP Ponds | 113453.96688087 |
| Sample size for Harvest revenue: overall TP Ponds | 5 |
| Mean Harvest revenue per ha: overall TC Ponds | 42646.0405022947 |
| Sample size for Harvest revenue: overall TC Ponds | 91 |
| Mean Harvest revenue per ha: overall PC Ponds | 93708.9894168062 |
| Sample size for Harvest revenue: overall PC Ponds | 66 |
| Mean Harvest revenue per ha: overall TPC Ponds | 106312.236886943 |
| Sample size for Harvest revenue: overall TPC Ponds | 81 |
| Mean Harvest revenue per ha: tilapia in TP ponds | 5974.47655532801 |
| Mean Harvest revenue per ha: tilapia in TC ponds | 17687.1584128919 |
| Mean Harvest revenue per ha: tilapia in PC ponds | NaN |
| Mean Harvest revenue per ha: tilapia in TPC ponds | 8428.79748664718 |
| Mean Harvest revenue per ha: pangasius in TP ponds | 101505.013770214 |
| Mean Harvest revenue per ha: pangasius in TC ponds | NaN |
| Mean Harvest revenue per ha: pangasius in PC ponds | 82539.3146454009 |
| Mean Harvest revenue per ha: pangasius in TPC ponds | 78315.7993815551 |
| Mean Harvest revenue per ha: carp in TP ponds | NaN |
| Mean Harvest revenue per ha: carp in TC ponds | 7271.72367651087 |
| Mean Harvest revenue per ha: carp in PC ponds | 11169.6747714053 |
| Mean Harvest revenue per ha: carp in TPC ponds | 14189.5199196007 |
| Mean Feed cost per ha: overall in TP ponds | -67952.2089166549 |
| Mean Feed cost per ha: overall in TC ponds | -12409.9899035514 |
| Mean Feed cost per ha: overall in PC ponds | -52373.9156371482 |
| Mean Feed cost per ha: overall in TPC ponds | -54612.1655933657 |
| Mean Feed cost per ha: tilapia in TP ponds | -3830.56715333192 |
| Mean Feed cost per ha: tilapia in TC ponds | -11557.8680454479 |
| Mean Feed cost per ha: tilapia in PC ponds | NaN |
| Mean Feed cost per ha: tilapia in TPC ponds | -5242.96532629625 |
| Mean Feed cost per ha: pangasius in TP ponds | -64121.641763323 |
| Mean Feed cost per ha: pangasius in TC ponds | NaN |
| Mean Feed cost per ha: pangasius in PC ponds | -50895.1188006685 |
| Mean Feed cost per ha: pangasius in TPC ponds | -47835.4843127779 |
| Mean Feed cost per ha: carp in TP ponds | NaN |
| Mean Feed cost per ha: carp in TC ponds | -1629.61201949324 |
| Mean Feed cost per ha: carp in PC ponds | -2474.92850013877 |
| Mean Feed cost per ha: carp in TPC ponds | -3178.5302169649 |
| Mean total cost per ha: in TP ponds | -71591.6002578973 |
| Mean total cost per ha: in TC ponds | -15663.3756387689 |
| Mean total cost per ha: in PC ponds | -55816.3148857085 |
| Mean total cost per ha: in TPC ponds | -58108.8514062526 |
| Mean total cost per ha: Overall | -41868.3906616489 |
| Mean BCR: tilapia in TP ponds | 1.09217230538303 |
| Mean BCR: tilapia in TC ponds | 1.63212015243018 |
| Mean BCR: tilapia in PC ponds | NaN |
| Mean BCR: tilapia in TPC ponds | 1.2418645318117 |
| Mean BCR: pangasius in TP ponds | 1.53813037671099 |
| Mean BCR: pangasius in TC ponds | NaN |
| Mean BCR: pangasius in PC ponds | 2.15646618252874 |
| Mean BCR: pangasius in TPC ponds | 2.01365613694991 |
| Mean BCR: carp in TP ponds | NaN |
| Mean BCR: carp in TC ponds | 3.15987911818643 |
| Mean BCR: carp in PC ponds | 3.68527571226089 |
| Mean BCR: carp in TPC ponds | 3.72206570137181 |
| Mean BCR: overall in TP ponds | 1.50727625858528 |
| Mean BCR: overall in TC ponds | 1.81604351142118 |
| Mean BCR: overall in PC ponds | 1.93026926680563 |
| Mean BCR: overall in TPC ponds | 1.78031676272959 |
| Mean Profit per tilapia in TP ponds | 0.0232093927272727 |
| Mean Profit per tilapia in TC ponds | 0.0993968380496016 |
| Mean Profit per tilapia in PC ponds | NaN |
| Mean Profit per tilapia in TPC ponds | 0.0537432735062702 |
| Mean Profit per pangasius in TP ponds | 0.579603461313 |
| Mean Profit per pangasius in TC ponds | NaN |
| Mean Profit per pangasius in PC ponds | 0.562938571857923 |
| Mean Profit per pangasius in TPC ponds | 0.602079818445303 |
| Mean Profit per carp in TP ponds | NaN |
| Mean Profit per carp in TC ponds | 0.818533256263477 |
| Mean Profit per carp in PC ponds | 0.822685469602784 |
| Mean Profit per carp in TPC ponds | 0.811073351406643 |
########################################################################
# Work out imputed costs = per hectare
########################################################################
# Length of pang cycle = 7 months, tilapia = 3.5 months
Pang_cycle <- 28
Carp_cycle <- 28
Tila_cycle <- 14
# Fingerling cost at start
StartUp_Pang_Hectare <- SurveyData[, mean(Finger_Cost_Pang_Hectare, na.rm = TRUE)]
StartUp_Tila_Hectare <- SurveyData[, mean(Finger_Cost_Tila_Hectare, na.rm = TRUE)]
StartUp_Carp_Hectare <- SurveyData[, mean(Finger_Cost_Carp_Hectare, na.rm = TRUE)]
# Feed cost per week PLUS farm costs
Weekly_Feed_Pang_Hectare <- SurveyData[, mean(Cost_Pang_Hectare, na.rm = TRUE)]/Pang_cycle
Weekly_Feed_Tila_Hectare <- SurveyData[, mean(Cost_Tila_Hectare, na.rm = TRUE)]/Tila_cycle
Weekly_Feed_Carp_Hectare <- SurveyData[, mean(Cost_Carp_Hectare_Real, na.rm = TRUE)]/Carp_cycle
FarmCost_Tila <- (sum(AddCosts$Tilapia.Price.per.ha)/Tila_cycle) * Conversion # per hectare per week
FarmCost_Pang <- (sum(AddCosts$Pangasius.price.per.ha)/Pang_cycle) * Conversion # per hectare per week
FarmCost_Carp <- (sum(AddCosts$Pangasius.price.per.ha)/Pang_cycle) * Conversion # per hectare per week
# Here are the interest rates
IntRate_10 <- 10/100
IntRate_5 <- 5/100
#############################################
# Run pang simulation - output in USD
#############################################
ModelCost_10 <- StartUp_Pang_Hectare
ModelCost_5 <- StartUp_Pang_Hectare
print(paste0("The start up cost - mean cost of pangasius fingerlings is: ", ModelCost_10))## [1] "The start up cost - mean cost of pangasius fingerlings is: -1389.61527651407"for (i in 1:Pang_cycle){
# Weekly interest rate
print(paste0("Pangasius Production Cycle Week ", i))
Interest_10 <- ModelCost_10 * (IntRate_10/52)
Interest_5 <- ModelCost_5 * (IntRate_5/52)
print(paste0("Interest cost at 10%: ", Interest_10))
#print(Interest_5)
ModelCost_10 <- ModelCost_10 + Interest_10 + Weekly_Feed_Pang_Hectare + FarmCost_Pang
ModelCost_5 <- ModelCost_5 + Interest_5 + Weekly_Feed_Pang_Hectare + FarmCost_Pang
print(paste("Cumulative cost for pangasius production per hectare with interest at 10% is:", ModelCost_10))
#print(paste("Total cost with interest at 5% is:", ModelCost_5))
}## [1] "Pangasius Production Cycle Week 1"
## [1] "Interest cost at 10%: -2.67233707021936"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -3128.21799660956"
## [1] "Pangasius Production Cycle Week 2"
## [1] "Interest cost at 10%: -6.01580383963377"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -4870.16418347447"
## [1] "Pangasius Production Cycle Week 3"
## [1] "Interest cost at 10%: -9.36570035283552"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -6615.46026685258"
## [1] "Pangasius Production Cycle Week 4"
## [1] "Interest cost at 10%: -12.7220389747165"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -8364.11268885258"
## [1] "Pangasius Production Cycle Week 5"
## [1] "Interest cost at 10%: -16.0848320939473"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -10116.1279039718"
## [1] "Pangasius Production Cycle Week 6"
## [1] "Interest cost at 10%: -19.4540921230227"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -11871.5123791201"
## [1] "Pangasius Production Cycle Week 7"
## [1] "Interest cost at 10%: -22.8298314983079"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -13630.2725936437"
## [1] "Pangasius Production Cycle Week 8"
## [1] "Interest cost at 10%: -26.212062680084"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -15392.415039349"
## [1] "Pangasius Production Cycle Week 9"
## [1] "Interest cost at 10%: -29.6007981525943"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -17157.9462205269"
## [1] "Pangasius Production Cycle Week 10"
## [1] "Interest cost at 10%: -32.9960504240902"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -18926.8726539763"
## [1] "Pangasius Production Cycle Week 11"
## [1] "Interest cost at 10%: -36.3978320268775"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -20699.2008690284"
## [1] "Pangasius Production Cycle Week 12"
## [1] "Interest cost at 10%: -39.8061555173624"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -22474.9374075711"
## [1] "Pangasius Production Cycle Week 13"
## [1] "Interest cost at 10%: -43.2210334760982"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -24254.0888240725"
## [1] "Pangasius Production Cycle Week 14"
## [1] "Interest cost at 10%: -46.6424785078316"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -26036.6616856056"
## [1] "Pangasius Production Cycle Week 15"
## [1] "Interest cost at 10%: -50.0705032415492"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -27822.6625718724"
## [1] "Pangasius Production Cycle Week 16"
## [1] "Interest cost at 10%: -53.5051203305238"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -29612.0980752282"
## [1] "Pangasius Production Cycle Week 17"
## [1] "Interest cost at 10%: -56.9463424523619"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -31404.9748007058"
## [1] "Pangasius Production Cycle Week 18"
## [1] "Interest cost at 10%: -60.3941823090497"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -33201.2993660402"
## [1] "Pangasius Production Cycle Week 19"
## [1] "Interest cost at 10%: -63.8486526270003"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -35001.0784016924"
## [1] "Pangasius Production Cycle Week 20"
## [1] "Interest cost at 10%: -67.3097661571008"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -36804.3185508748"
## [1] "Pangasius Production Cycle Week 21"
## [1] "Interest cost at 10%: -70.7775356747593"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -38611.0264695748"
## [1] "Pangasius Production Cycle Week 22"
## [1] "Interest cost at 10%: -74.2519739799516"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -40421.2088265801"
## [1] "Pangasius Production Cycle Week 23"
## [1] "Interest cost at 10%: -77.7330938972694"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -42234.8723035026"
## [1] "Pangasius Production Cycle Week 24"
## [1] "Interest cost at 10%: -81.2209082759666"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -44052.0235948039"
## [1] "Pangasius Production Cycle Week 25"
## [1] "Interest cost at 10%: -84.7154299900074"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -45872.6694078191"
## [1] "Pangasius Production Cycle Week 26"
## [1] "Interest cost at 10%: -88.2166719381137"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -47696.8164627825"
## [1] "Pangasius Production Cycle Week 27"
## [1] "Interest cost at 10%: -91.7246470438126"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -49524.4714928516"
## [1] "Pangasius Production Cycle Week 28"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost for pangasius production per hectare with interest at 10% is: -51355.6412441324"CumInterest_Pang <- ModelCost_10 - (StartUp_Pang_Hectare) - (Pang_cycle * (Weekly_Feed_Pang_Hectare+ FarmCost_Pang))
print(paste0("The cumulative interest cost per hectare for pangasius is: ",CumInterest_Pang))## [1] "The cumulative interest cost per hectare for pangasius is: -1359.97524291057"print(paste0("For an average farm the capital risk for pangasius is: ",(ModelCost_10 * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For an average farm the capital risk for pangasius is: -15797.6398339618 per farm."print(paste0("For the average farm in the study this interest costs : ", (CumInterest_Pang * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For the average farm in the study this interest costs : -418.345454367403 per farm."##################################
# Run tila simulation
##################################
Tila_ModelCost_10 <- StartUp_Tila_Hectare
Tila_ModelCost_5 <- StartUp_Tila_Hectare
print(paste0("The start up cost - mean cost of tilapia fingerlings is: ", Tila_ModelCost_10))## [1] "The start up cost - mean cost of tilapia fingerlings is: -376.364862035877"for (i in 1:Tila_cycle){
# Weekly interest rate
print(paste0("Tilapia Production Cycle Week ", i))
Tila_Interest_10 <- Tila_ModelCost_10 * (IntRate_10/52)
Tila_Interest_5 <- Tila_ModelCost_5 * (IntRate_5/52)
print(paste0("Interest cost at 10%: ", Interest_10))
#print(Tila_Interest_5)
Tila_ModelCost_10 <- Tila_ModelCost_10 + Tila_Interest_10 + Weekly_Feed_Tila_Hectare + FarmCost_Tila
Tila_ModelCost_5 <- Tila_ModelCost_5 + Tila_Interest_5 + Weekly_Feed_Tila_Hectare + FarmCost_Tila
print(paste("Cumulative cost tilapia with interest at 10% is:", Tila_ModelCost_10))
#print(paste("Total cost tilapia with interest at 5% is:", Tila_ModelCost_5))
}## [1] "Tilapia Production Cycle Week 1"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -814.198369887628"
## [1] "Tilapia Production Cycle Week 2"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -1252.87386525448"
## [1] "Tilapia Production Cycle Week 3"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -1692.39296734319"
## [1] "Tilapia Production Cycle Week 4"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -2132.75729847438"
## [1] "Tilapia Production Cycle Week 5"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -2573.96848408851"
## [1] "Tilapia Production Cycle Week 6"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -3016.0281527519"
## [1] "Tilapia Production Cycle Week 7"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -3458.93793616272"
## [1] "Tilapia Production Cycle Week 8"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -3902.69946915702"
## [1] "Tilapia Production Cycle Week 9"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -4347.31438971478"
## [1] "Tilapia Production Cycle Week 10"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -4792.78433896591"
## [1] "Tilapia Production Cycle Week 11"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -5239.11096119637"
## [1] "Tilapia Production Cycle Week 12"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -5686.2959038542"
## [1] "Tilapia Production Cycle Week 13"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -6134.34081755561"
## [1] "Tilapia Production Cycle Week 14"
## [1] "Interest cost at 10%: -95.2393682554839"
## [1] "Cumulative cost tilapia with interest at 10% is: -6583.24735609105"CumInterest <- Tila_ModelCost_10 - (StartUp_Tila_Hectare) - (Tila_cycle * (Weekly_Feed_Tila_Hectare+ FarmCost_Tila))
print(paste0("The cumulative interest cost per hectare for tilapia is: ", CumInterest))## [1] "The cumulative interest cost per hectare for tilapia is: -87.3462842623885"print(paste0("For an average farm the capital risk for tilapia is: ",(Tila_ModelCost_10 * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For an average farm the capital risk for tilapia is: -2025.08951596997 per farm."print(paste0("For the average farm in the study this interest costs : ", (CumInterest * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For the average farm in the study this interest costs : -26.8688133607856 per farm."#############################################
# Run carp simulation - output in USD
#############################################
Carp_ModelCost_10 <- StartUp_Carp_Hectare
Carp_ModelCost_5 <- StartUp_Carp_Hectare
print(paste0("The start up cost - mean cost of carp fingerlings is: ", Carp_ModelCost_10))## [1] "The start up cost - mean cost of carp fingerlings is: -284.196649789084"for (i in 1:Carp_cycle){
# Weekly interest rate
print(paste0("Carp Production Cycle Week ", i))
Interest_10 <- Carp_ModelCost_10 * (IntRate_10/52)
Interest_5 <- Carp_ModelCost_5 * (IntRate_5/52)
print(paste0("Interest cost at 10%: ", Interest_10))
#print(Interest_5)
Carp_ModelCost_10 <- Carp_ModelCost_10 + Interest_10 + Weekly_Feed_Carp_Hectare + FarmCost_Carp
Carp_ModelCost_5 <- Carp_ModelCost_5 + Interest_5 + Weekly_Feed_Carp_Hectare + FarmCost_Carp
print(paste("Cumulative cost for carp production per hectare with interest at 10% is:", Carp_ModelCost_10))
#print(paste("Total cost with interest at 5% is:", ModelCost_5))
}## [1] "Carp Production Cycle Week 1"
## [1] "Interest cost at 10%: -0.546532018825162"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -329.898467393397"
## [1] "Carp Production Cycle Week 2"
## [1] "Interest cost at 10%: -0.634420129602687"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -375.688173108488"
## [1] "Carp Production Cycle Week 3"
## [1] "Interest cost at 10%: -0.722477255977862"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -421.565935949954"
## [1] "Carp Production Cycle Week 4"
## [1] "Interest cost at 10%: -0.810703722980681"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -467.531925258423"
## [1] "Carp Production Cycle Week 5"
## [1] "Interest cost at 10%: -0.899099856266198"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -513.586310700177"
## [1] "Carp Production Cycle Week 6"
## [1] "Interest cost at 10%: -0.987665982115725"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -559.729262267781"
## [1] "Carp Production Cycle Week 7"
## [1] "Interest cost at 10%: -1.07640242743804"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -605.960950280707"
## [1] "Carp Production Cycle Week 8"
## [1] "Interest cost at 10%: -1.16530951977059"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -652.281545385966"
## [1] "Carp Production Cycle Week 9"
## [1] "Interest cost at 10%: -1.2543875872807"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -698.691218558734"
## [1] "Carp Production Cycle Week 10"
## [1] "Interest cost at 10%: -1.3436369587668"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -745.190141102989"
## [1] "Carp Production Cycle Week 11"
## [1] "Interest cost at 10%: -1.43305796365959"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -791.778484652137"
## [1] "Carp Production Cycle Week 12"
## [1] "Interest cost at 10%: -1.52265093202334"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -838.456421169648"
## [1] "Carp Production Cycle Week 13"
## [1] "Interest cost at 10%: -1.61241619455702"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -885.224122949693"
## [1] "Carp Production Cycle Week 14"
## [1] "Interest cost at 10%: -1.70235408259556"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -932.081762617777"
## [1] "Carp Production Cycle Week 15"
## [1] "Interest cost at 10%: -1.79246492811111"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -979.029513131376"
## [1] "Carp Production Cycle Week 16"
## [1] "Interest cost at 10%: -1.88274906371418"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1026.06754778058"
## [1] "Carp Production Cycle Week 17"
## [1] "Interest cost at 10%: -1.97320682265496"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1073.19604018872"
## [1] "Carp Production Cycle Week 18"
## [1] "Interest cost at 10%: -2.06383853882446"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1120.41516431303"
## [1] "Carp Production Cycle Week 19"
## [1] "Interest cost at 10%: -2.15464454675583"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1167.72509444528"
## [1] "Carp Production Cycle Week 20"
## [1] "Interest cost at 10%: -2.24562518162553"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1215.12600521239"
## [1] "Carp Production Cycle Week 21"
## [1] "Interest cost at 10%: -2.3367807792546"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1262.61807157713"
## [1] "Carp Production Cycle Week 22"
## [1] "Interest cost at 10%: -2.42811167610987"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1310.20146883873"
## [1] "Carp Production Cycle Week 23"
## [1] "Interest cost at 10%: -2.51961820930525"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1357.87637263352"
## [1] "Carp Production Cycle Week 24"
## [1] "Interest cost at 10%: -2.61130071660293"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1405.64295893562"
## [1] "Carp Production Cycle Week 25"
## [1] "Interest cost at 10%: -2.70315953641465"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1453.50140405752"
## [1] "Carp Production Cycle Week 26"
## [1] "Interest cost at 10%: -2.79519500780292"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1501.45188465081"
## [1] "Carp Production Cycle Week 27"
## [1] "Interest cost at 10%: -2.88740747048232"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1549.49457770678"
## [1] "Carp Production Cycle Week 28"
## [1] "Interest cost at 10%: -2.97979726482073"
## [1] "Cumulative cost for carp production per hectare with interest at 10% is: -1597.62966055709"CumInterest_Carp <- Carp_ModelCost_10 - (StartUp_Carp_Hectare) - (Carp_cycle * (Weekly_Feed_Carp_Hectare+ FarmCost_Carp))
print(paste0("The cumulative interest cost per hectare for Carp is: ",CumInterest_Carp))## [1] "The cumulative interest cost per hectare for Carp is: -49.0850143743376"print(paste0("For an average farm the capital risk for Carp is: ",(Carp_ModelCost_10 * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For an average farm the capital risk for Carp is: -491.450936140713 per farm."print(paste0("For the average farm in the study this interest costs : ", (CumInterest_Carp * (SurveyData[, mean(Pond.surface.area, na.rm = TRUE)] * Dec2Hec)), " per farm."))## [1] "For the average farm in the study this interest costs : -15.0991665091752 per farm."Here are the plots for the Aquaculture International Paper and a few extra supplementary plots.
#########################################
# Plot of stock density
#########################################
library(plotly)## Warning: package 'plotly' was built under R version 4.0.5## Loading required package: ggplot2## Warning: package 'ggplot2' was built under R version 4.0.5##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutTotal_Stock_Density <- SurveyData$Overall_Density
Tila_Stock_Density <- SurveyData$Number.of.Tilapia.Stocked/pond_surface_area## Warning in SurveyData$Number.of.Tilapia.Stocked/pond_surface_area: longer object
## length is not a multiple of shorter object lengthCarp_Stock_Density <- SurveyData$Number.of.Carp.Fish.Stocked/pond_surface_area## Warning in SurveyData$Number.of.Carp.Fish.Stocked/pond_surface_area: longer
## object length is not a multiple of shorter object lengthPang_Stock_Density <- SurveyData$Number.of.Pangasius.Stocked/pond_surface_area## Warning in SurveyData$Number.of.Pangasius.Stocked/pond_surface_area: longer
## object length is not a multiple of shorter object lengthStock_Box <- plot_ly(type = 'violin') %>%
add_trace(x ="Total", y = Total_Stock_Density,
fillcolor = 'rgb(200,200,200)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)'),
width = 0.7,
box = list(visible = T),
meanline = list(visible = T),
points = "outliers", jitter = 1, pointpos = 0) %>%
add_trace(x ="Carp", y = Carp_Stock_Density,
fillcolor = 'rgb(180,180,180)',
width = 0.7,
box = list(visible = T),
meanline = list(visible = T),
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)'),
points = "outliers", jitter = 1, pointpos = 0) %>%
add_trace(x ="Tilapia", y = Tila_Stock_Density,
fillcolor = 'rgb(200,200,200)',
width = 0.7,
box = list(visible = T),
meanline = list(visible = T),
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)'),
points = "outliers", jitter = 1, pointpos = 0) %>%
add_trace(x ="Pangasius", y = Pang_Stock_Density,
fillcolor = 'rgb(200,200,200)',
width = 0.7,
box = list(visible = T),
meanline = list(visible = T),
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)'),
points = "outliers", jitter = 1, pointpos = 0) %>%
layout(showlegend = FALSE,
xaxis = list(title = "Stock", showline = TRUE, zeroline = FALSE),
yaxis = list(title = "Stocking Density (Fish m<sup>-2</sup>)", showline = TRUE, zeroline = FALSE))
FigFolder <- "C:\\Users\\rh21\\OneDrive - CEFAS\\Project Work\\C6771A - Bangladesh Farm Survey\\Aquaculture Submission\\Figures"
orca(Stock_Box, file = "StockDensities.svg")## Warning: Ignoring 5 observations## Warning: Ignoring 67 observations## Warning: Ignoring 92 observations## Warning: Can't display both discrete & non-discrete data on same axis#library(magick)
#im <- image_read_svg("Fig4.svg")
#image_write(im, "Fig4.jpeg", format = "jpeg")
##############################################################
# Average weight gain per fish
##############################################################
WtGain_Tila <- (SurveyData$Tilapia.Weight.at.Harvest..g. - SurveyData$Tilapia.Fingerlings.Weight..g.) / 1000
WtGain_Pang <- (SurveyData$Pangasius.Weight.at.Harvest..g. - SurveyData$Pangasius.Fingerlings.Weight..g.) / 1000
WtGain_Carp <- (SurveyData$Carp.Weight.at.Harvest..g. - SurveyData$Carp.Fingerlings.Weight..g.) / 1000
Prod_DF <- data.frame(cbind(WtGain_Tila, WtGain_Pang, WtGain_Carp))
scat_WtGain <- plot_ly(type = 'box', data = Prod_DF) %>%
add_trace(x = "Pangasius",
y = ~WtGain_Pang,
name = 'Pangasius',
boxmean = 'sd',
fillcolor = 'rgb(200,200,200)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = "Tilapia",
y = ~WtGain_Tila,
name = 'Tilapia',
boxmean = 'sd',
fillcolor = 'rgb(150,150,150)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = "Carp",
y = ~WtGain_Carp,
boxmean = 'sd',
name = 'Carp',
fillcolor = 'rgb(60,60,60)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
layout(showlegend = FALSE,
xaxis = list(title = "Species group cultured", showline = TRUE),
yaxis = list(title = "Biomass increase per fish (kg)", showline = TRUE, range = c(0,2)))
orca(scat_WtGain, file = "BiomassIncrease.svg")## Warning: Ignoring 95 observations## Warning: Ignoring 76 observations## Warning: Ignoring 29 observations## Warning: Can't display both discrete & non-discrete data on same axis##############################################################
# Plot pang productivity versus tila productivity
# Need to stratify to ponds with just tila+pang only
##############################################################
# Get the productivity values - here the gain in weight over the harvest * fish present / area of pond
# Final value is tonnes/hectare (assume 1 harvest for pang/2 for tilapia and 1 for carp)
prod_Tila <- (((SurveyData$Tilapia.Weight.at.Harvest..g. - SurveyData$Tilapia.Fingerlings.Weight..g.) * SurveyData$Number.of.Tilapia.Stocked) / (SurveyData$Pond.surface.area * 40.46)) * 0.01 * 2
prod_Pang <- (((SurveyData$Pangasius.Weight.at.Harvest..g. - SurveyData$Pangasius.Fingerlings.Weight..g.) * SurveyData$Number.of.Pangasius.Stocked) / (SurveyData$Pond.surface.area * 40.46))* 0.01
prod_Carp <- (((SurveyData$Carp.Weight.at.Harvest..g. - SurveyData$Carp.Fingerlings.Weight..g.) * SurveyData$Number.of.Carp.Fish.Stocked) / (SurveyData$Pond.surface.area * 40.46))* 0.01
prod_Total <- rowSums(cbind(prod_Tila, prod_Pang, prod_Carp), na.rm = TRUE)
prod_Total[prod_Total == 0] <- NA
# Get the types of polyculture (TP = Tila&Pang; TC = Tila&Carp; PC = Pang&Carp; TPC = Tila&Pang&Carp)
PolyCult <- SurveyData$PolyCult
Prod_DF <- data.frame(cbind(PolyCult, prod_Tila, prod_Pang, prod_Carp, prod_Total))
scat_Prod <- plot_ly(type = 'box', data = Prod_DF) %>%
add_trace(x = ~PolyCult,
y = ~prod_Total,
name = 'Total',
fillcolor = 'rgb(255,255,255)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Pang,
name = 'Pangasius',
fillcolor = 'rgb(200,200,200)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Tila,
name = 'Tilapia',
fillcolor = 'rgb(150,150,150)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Carp,
name = 'Carp',
fillcolor = 'rgb(60,60,60)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
layout('boxmode' = 'group',
xaxis = list(title = "Type of Polyculture Employed",
showline = TRUE),
yaxis = list(title = "Biomass increase (g)",
showline = TRUE)
)
orca(scat_WtGain, file = "ProductitySpeciesProductionCycle.svg")## Warning: Ignoring 95 observations## Warning: Ignoring 76 observations## Warning: Ignoring 29 observations## Warning: Can't display both discrete & non-discrete data on same axis##################################################################
# Influence of disease and mortality
##################################################################
MortLevel <- data.frame(cbind(SurveyData$Pangasius.Stock.Mortality.during.Production,
SurveyData$Tilapia.Stock.Mortality.during.Production,
SurveyData$Carp.Stock.Mortality.during.Production))
MortLevel[MortLevel == 0] <- 1
MortLevel[MortLevel == 1] <- 0.95
MortLevel[MortLevel == 2] <- 0.85
MortLevel[MortLevel == 3] <- 0.4
AdjProd_DF <- data.frame(cbind(PolyCult, prod_tila = (prod_Tila * MortLevel[, 2]),
prod_Pang = (prod_Pang * MortLevel[, 1]), prod_Carp = (prod_Carp * MortLevel[, 3])))
AdjTotalProd <- rowSums(cbind((prod_Tila * MortLevel[, 2]), (prod_Pang * MortLevel[, 1]), (prod_Carp * MortLevel[, 3])), na.rm = TRUE)
AdjTotalProd[AdjTotalProd == 0] <- NA
AdjProd_DF <- cbind(AdjProd_DF, AdjTotalProd)
scat_Prod <- plot_ly(type = 'box', data = SurveyData) %>%
add_trace(x = ~PolyCult,
y = ~prod_Total_TPC,
name = 'Total Yield',
fillcolor = 'rgb(255,255,255)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Pang,
name = 'Pangasius Yield',
fillcolor = 'rgb(50,50,50)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Tila,
name = 'Tilapia Yield',
fillcolor = 'rgb(210,210,210)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
add_trace(x = ~PolyCult,
y = ~prod_Carp,
name = 'Carp Yield',
fillcolor = 'rgb(160,160,160)',
line = list(color = 'rgb(20,20,20)'),
marker = list(color = 'rgb(20,20,20)')) %>%
layout('boxmode' = 'group', bargap = 0.1,
legend = list(x = 0.85, y = 0.97, borderwidth = 1),
xaxis = list(title = "Species group farmed",
tickmode = 'array', tickvals = c("TPC", "PC", "TP", "TC"),
ticktext = c("Pangasius & Tilapia & Carp",
"Pangasius & Carp",
"Pangasius & Tilapia",
"Tilapia & Carp"),
categoryorder = "array",
categoryarray = c("TPC", "PC", "TP", "TC"),
showline = TRUE, showdividers = TRUE),
yaxis = list(title = "Yield (tonnes ha<sup>-1</sup>)",
showline = TRUE,
range = c(0, 165))
)
orca(scat_Prod, file = "YieldByPolycultureType.svg", scale = 2, width =600, height = 800)## Warning: Ignoring 3 observations## Warning: Ignoring 95 observations## Warning: Ignoring 76 observations## Warning: Ignoring 29 observations## Warning: 'layout' objects don't have these attributes: 'boxmode'
## Valid attributes include:
## 'font', 'title', 'uniformtext', 'autosize', 'width', 'height', 'margin', 'computed', 'paper_bgcolor', 'plot_bgcolor', 'separators', 'hidesources', 'showlegend', 'colorway', 'datarevision', 'uirevision', 'editrevision', 'selectionrevision', 'template', 'modebar', 'newshape', 'activeshape', 'meta', 'transition', '_deprecated', 'clickmode', 'dragmode', 'hovermode', 'hoverdistance', 'spikedistance', 'hoverlabel', 'selectdirection', 'grid', 'calendar', 'xaxis', 'yaxis', 'ternary', 'scene', 'geo', 'mapbox', 'polar', 'radialaxis', 'angularaxis', 'direction', 'orientation', 'editType', 'legend', 'annotations', 'shapes', 'images', 'updatemenus', 'sliders', 'colorscale', 'coloraxis', 'metasrc', 'barmode', 'bargap', 'mapType'Here is a plot of the stocking densities:
Here is a plot of the weight gain for each species:
Here is a plot of the productivity of the species based on 2 cycles of tilapia versus 1 production cycle of pangasius and carp.
Here is a plot of the productity corrected for mortality in the pond.